Risk Assessment of Norovirus on Shellfish from Indonesian

Risk Assessment of Norovirus on Shellfish from

Indonesian Fish Markets

By

Radestya Triwibowo

B.Sc. Aquatic Resource Management

Padjadjaran University, Indonesia, 2005

Submitted in fulfilment of the requirements for the Doctor of Philosophy

University of Tasmania, October, 2019

i

Declaration of Originality

"This thesis contains no material which has been accepted for a degree or diploma by the University

or any other institution, except by way of background information and duly acknowledged in the

thesis, and to the best of my knowledge and belief no material previously published or written by

another person except where due acknowledgement is made in the text of the thesis, nor does the

thesis contain any material that infringes copyright.”

Radestya Triwibowo October 2019

Authority of Access

This thesis may be made available for loan and limited copying in accordance with the Copyright Act

1968.

Radestya Triwibowo October 2019

ii

Statement of Co-Authorship

This thesis comprises of work that has been prepared to be submitted to journals. Information for

each chapter is provided in the section of communications arising from this thesis.

The following people and institutions contributed to the publication and preparation of the work

undertaken as part of this thesis:

Radestya Triwibowo, Tasmanian Institute of Agriculture, University of Tasmania (Candidate)

Tom Ross, Tasmanian Institute of Agriculture, University of Tasmania (Supervisor)

Shane M. Powell, Tasmanian Institute of Agriculture, University of Tasmania (Co-Supervisor)

Chawalit Kocharunchitt, Tasmanian Institute of Agriculture, University of Tasmania (Co-Supervisor)

Communication Arising from this Thesis

Prepared paper for publications

Triwibowo, R., Powell, S., Kocharunchitt, C., and Ross, T. Improving molecular quantification of viable

MS2 bacteriophage: a Norovirus surrogate for inactivation studies.

Journal article in preparation.

Author 1 contributed 70% (designed the experiment, optimized the improved method, conducted

laboratory analysis, analysed the data and wrote the manuscript), author 2, 3 and 4 each

contributed 10% (provided suggestion for the experimental design, contributed to the development

of molecular method and provided input for the manuscript).

We the undersigned agree with the above stated "proportion of work undertaken" for the above

prepared peer-reviewed manuscript contribute to this thesis.

Signed

(Prof. Tom Ross)

Primary Supervisor

Tasmanian Institute of Agriculture

University of Tasmania

Date: February 27th, 2019

(Prof. Holger Meinke)

Director

Tasmanian Institute of Agriculture

University of Tasmania

iii

iv

Presentations from this thesis

1. Triwibowo, R., Powell, S., Kocharunchitt, C., and Ross, T. 2016. Improving quantification of MS2

bacteriophage: a norovirus surrogate. 4th Asia-Pacific International Food Safety Conference & 7th

Asian Conference on Food and Nutrition Safety, October 11 – 13, 2016, Penang, Malaysia.

(Poster presentation).

2. Triwibowo, R., Ross, T., Powell, S., and Kocharunchitt, C. 2017. The application of enzymatic pre-

treatment to improve PCR assay quantification of NoV surrogate. The Australian Society for

Microbiology (ASM) Conference, July 2 – 5, 2017, Hobart, Australia. (Oral presentation)

3. Triwibowo, R., Kocharunchitt, C., Powell, S., and Ross, T. 2018. Prevalence of norovirus (NoV) GII

in shellfish from Indonesian fish markets. The New Zealand Institute of Food Science and

Technology (NZIFST) Annual Conference, July 3 – 5, 2018, Hamilton, New Zealand. (Poster

presentation).

v

Acknowledgment

I would like to express my deep gratitude to the following people who contributed to this thesis and

supported me along the way.

First and foremost, I would like to thank my supervisors Prof. Tom Ross, Dr. Shane Powell and Dr.

Chawalit ‘Jay’ Kocharunchitt of the Tasmanian Institute of Agriculture (TIA) for sharing their

extensive knowledge and experience on food microbiology and food safety with me. I deeply thank

them for the countless time they spent to guide my research, for their patient and understanding,

and for their support in every aspect of my PhD journey.

I thank the director of the Research and Development Centre for Marine and Fisheries Product

Processing and Biotechnology (RDCMFPPB), the Indonesian Ministry of Marine Affairs and Fisheries,

Prof. Hari Eko Irianto as well as the former directors, Prof. Agus Heri Purnomo and Ir. Nugroho Aji,

MSi for their support during my study. To my fellow research scientists at the fisheries safety

research group; and to Research Professor Dr. Endang Sri Heruwati who inspire me to keep learning

on new things. Many thanks to the technical and non-technical staffs of Microbiology and

Biotechnology Laboratories at the RDCMFPPB; Mr. Budi, Mr Iksan Darmawan, Ms Anggi Musvita.

I would also like to acknowledge the Australia Awards Scholarship (AAS) and TIA scholarship that

provided financial support during my study.

I had great pleasure to work with Mr. Adam Smolenski and Ms. Sharee McCammon of the Molecular

Laboratory, the Central Science Laboratory (CSL). I thank them for their kind assistance during my

laboratory work. I thank Ms. Michelle Williams, Mr. Anthony Baker, Ms. Lauri Parkinson and Ms.

Joanne Pagnon of TIA Microbiology Research Laboratory for their support throughout the years.

My sincere gratitude to Honorary Professor, Dr. David Ratkowsky of TIA for his guidance and insight

on mathematic and statistics; and to Prof. John Bowman of the Centre of Food Safety and Innovation

(CFSI) for his ideas and suggestions about my research as well as his support on the technical

matters of my laboratory work. It has been an honour for me to know them in person.

I would like to extend my appreciation to the members of CFSI, TIA. Prof. Mark Tamplin, Prof.

Thomas McMeekin, Dr. Mandeep Kaur, Dr. Ross Corkrey, Dr. Alieta Eyles, Dr. Lyndall Mellefont. And

to the other PhD candidates at CFSI, Ms. Akhikun Nahar, Ms. Fera Roswita Dewi, Ms. Kaniz Mohsina,

Mr. Kayode Adu, Mr. Tai Gardner, Dr. Tuflikha Putri, Ms. Vongai Dakwa, Mr. Zachary Block; as well as

my PhD office-mates Ms. Elya Richardson, Ms. Gail Gnoinski, Mr. Ha Duong-Nam, Mr. Sohail Ayyaz,

and Mr. Quang Truong. From these people, I learned enormous experience that comes from

different perspectives and backgrounds.

vi

My study at the University of Tasmania will not be as smooth without assistance from the former

and current graduate research coordinators; A/Prof. Aduli Malau-Aduli, A/Prof. Calum Wilson and

Dr. Lana Shabala, as well as the research development advisor, Ms. Brooke Vanderlaan. I also thank

Mr. Christopher Dillon, Ms. Andrea Riseley, Ms. Kathleen Hinds, and Ms. Sharmila Prajit of the UTAS

International Scholarship Officer; Ms. Chantal Woodhams, Ms. Amanda Winter and Ms. Tina Bailey

of TIA; Ms. Morag Porteous and Mrs. Louise Oxley of UTAS Student Learning; Ms. Heather Mitchell

as the research librarian; who provided academic and technical supports throughout my study.

Thanks should also go to my friends at the TUU Moslem Society, the Tasmanian Indonesian Student

Association and my fishing buddies (Aswardi, Purwadi, Alba, Arie, Andika, Andrew, Wajiran) who

made my stay in Hobart more fun and memorable.

I deeply thank my parents and my parents-in-law, Meidy Hermawan, Bambang Widjoseno,

Rumartiatun, Ghozali, Suliyati, for their advice, endless supports and prayers. I am very blessed to

have continuous supports from my brothers and sisters, Ratih, Anung, Dipto, Ratri, Yuntha, Uci, Sita,

Ami, Puput, Hendra, Tria, Anis, Anas, as well as from my nieces and nephews.

Finally, yet importantly, I want to thank my wife, Novalia Rachmawati, the best companion in this

PhD journey and the best partner to share my ideas about work and life adventure. She deserves my

deepest gratitude for her patience, love and prayers during our past, present and future journey.

vii

Table of Content

Declaration of Originality .............................................................................................................. i

Statement of Co-Authorship ......................................................................................................... ii

Communication Arising from this Thesis ...................................................................................... iii

Acknowledgment ......................................................................................................................... v

Table of Content ........................................................................................................................ vii

List of Figures ............................................................................................................................. xii

List of Tables .............................................................................................................................. xv

List of Abbreviations ................................................................................................................. xvii

Abstract ..................................................................................................................................... xx

Chapter 1. Literature review .................................................................................................... 1

1.1. Introduction .................................................................................................................................. 1

1.1.1. Human enteric viruses .......................................................................................................... 3

1.1.2. Human norovirus .................................................................................................................. 5

1.1.3. Structure and biology of norovirus ....................................................................................... 6

1.1.4. Foodborne norovirus related diseases ................................................................................. 9

1.1.5. NoV in shellfish ................................................................................................................... 10

1.2. Bivalve molluscan shellfish ......................................................................................................... 10

1.2.1. Biology of shellfish .............................................................................................................. 10

1.2.2. Shellfish production ............................................................................................................ 11

1.2.3. Shellfish in Indonesia .......................................................................................................... 12

1.3. Detection and quantification methods for noroviruses ............................................................. 15

viii

1.3.1. Primer sequences for detection, genotyping and quantification of NoV by RT-qPCR ....... 16

1.3.2. Sample pre-treatment in NoV inactivation studies to differentiate infectious/non-

infectious viruses ................................................................................................................ 20

1.4. Inactivation of human NoV in shellfish ....................................................................................... 22

1.4.1. NoV inactivation studies using surrogates ......................................................................... 22

1.4.2. Chlorination ........................................................................................................................ 24

1.4.3. High temperature treatment .............................................................................................. 25

1.4.4. Mathematical modelling on virus inactivation ................................................................... 25

1.5. Risk assessment of human NoV in shellfish ................................................................................ 29

1.6. Thesis objectives ......................................................................................................................... 29

Chapter 2. Improving molecular quantification of infectious MS2 bacteriophage: A norovirus

surrogate for inactivation studies ................................................................................... 31

2.1. Introduction ................................................................................................................................ 31

2.2. Materials and methods ............................................................................................................... 33

2.2.1. MS2 bacteriophage stock production................................................................................. 33

2.2.2. Quantification of MS2 ......................................................................................................... 34

2.2.2.1. Plaque assay .................................................................................................................. 34

2.2.2.2. RT-qPCR development ................................................................................................... 34

2.2.3. Preliminary experiment ...................................................................................................... 36

2.2.4. Development of pre-treatment for RT-qPCR ...................................................................... 37

2.2.5. Application of pre-treatment RT-qPCR for inactivation studies ......................................... 37

2.3. Results ......................................................................................................................................... 38

2.3.1. The correlation between plaque assay and RT-qPCR ......................................................... 38

2.3.2. Effect of different pre-treatments on the quantification of mixtures of infectious and non-

infectious MS2 .................................................................................................................... 39

2.3.3. The application of RT-qPCR with pre-treatment in inactivation study ............................... 42

2.4. Discussion ................................................................................................................................... 44

ix

2.5. Conclusions ................................................................................................................................. 49

Chapter 3. Thermal inactivation kinetics of Human norovirus and MS2 bacteriophage in

buffered media and bioaccumulated Tasmanian Blue Mussel (Mytilus galloprovincialis) . 50

3.1. Introduction ................................................................................................................................ 50


3.2.1. NoV stock preparation ........................................................................................................ 52

3.2.2. MS2 bacteriophage stock production................................................................................. 53

3.2.3. Bioaccumulation in mussels ............................................................................................... 53

3.2.4. Thermal inactivation in buffered media ............................................................................. 54

3.2.5. Thermal inactivation in mussel matrix ............................................................................... 56

3.2.6. Enumeration of NoV and MS2 ............................................................................................ 56

3.2.6.1. Virus concentration ....................................................................................................... 56

3.2.6.2. Enzymatic pre-treatment prior to RNA extraction ........................................................ 57

3.2.6.3. Quantification of infectious NoV by RT-qPCR assay ...................................................... 57

3.2.6.4. Quantification of infectious MS2 by plaque assay ........................................................ 58

3.2.7. Modelling of thermal inactivation kinetics ......................................................................... 58

3.2.8. Statistical analysis ............................................................................................................... 59

3.3. Results ......................................................................................................................................... 60

3.3.1. Bioaccumulation of NoV and MS2 in mussel ...................................................................... 60

3.3.2. Thermal inactivation of NoV and MS2 ................................................................................ 61

3.3.3. Model fitting and comparison ............................................................................................ 61

3.3.4. The z curves of NoV and MS2 thermal inactivation............................................................ 67

3.4. Discussion ................................................................................................................................... 71

3.5. Conclusions ................................................................................................................................. 74

Chapter 4. Chlorine dioxide inactivation of NoV and MS2 in buffered media and artificially

contaminated Tasmanian Blue Mussels (Mytilus galloprovincialis) tissue ........................ 76

x

4.1. Introduction ................................................................................................................................ 76


4.2.1. Mussels preparation and artificial contamination. ............................................................ 79

4.2.2. Chlorine dioxide treatments ............................................................................................... 79

4.2.3. Analysis of ClO2 residue by Palintest kit ............................................................................. 81

4.2.4. Virus and bacteriophage purification ................................................................................. 81

4.2.5. Enumeration of MS2 by plaque assay ................................................................................ 81

4.2.6. Virus pre-treatment and RNA extraction ............................................................................ 81

4.2.7. Enumeration of NoV by RT-qPCR ........................................................................................ 81

4.2.8. Modelling and statistical analysis of ClO2 inactivation kinetics .......................................... 82

4.3. Results ......................................................................................................................................... 83

4.3.1. ClO2 decay in buffered media and mussel matrix .............................................................. 83

4.3.2. The efficacy of ClO2 treatment on NoV and MS2 in buffered media ................................. 85

4.3.3. The efficacy of ClO2 treatment on NoV and MS2 in mussel matrix .................................... 87

4.4. Discussion ................................................................................................................................... 90

4.5. Conclusion .................................................................................................................................. 94

Chapter 5. Risk assessment of NoV GII in shellfish from Indonesian fish markets .................... 96

5.1. Introduction ................................................................................................................................ 96


5.2.1. Sample collection from Indonesian fish markets in Jakarta and Panimbang. .................... 98

5.2.2. Viral extraction and purification from shellfish digestive tissues ..................................... 100

5.2.3. Plaque assay method to determine viral extraction efficiency ........................................ 100

5.2.4. RNase pre-treatment and RNA extraction ........................................................................ 100

5.2.5. Enumeration of NoV by RT-qPCR ...................................................................................... 101

5.2.6. Statistical analysis ............................................................................................................. 102

5.2.7. Genotyping ....................................................................................................................... 102

xi

5.2.8. Quantitative risk assessment of NoV in shellfish from Indonesian markets .................... 103

5.3. Results ....................................................................................................................................... 106

5.3.1. NoV exposure from shellfish from Indonesian fish markets ............................................ 106

5.3.1.1. The efficiency of virus extraction and RNase pre-treatment process ......................... 107

5.3.1.2. NoV prevalence and enumeration in the shellfish from Indonesian fish markets ...... 108

5.3.2. Genotyping of NoV GII isolated from contaminated shellfish .......................................... 109

5.4. Discussion ................................................................................................................................. 110

5.4.1. Prevalence and contamination levels of NoV in shellfish from Indonesian fish markets 110

5.4.2. Quantitative Risk Assessment of NoV in Shellfish from Indonesian markets ................... 112

5.4.2.1. Hazard identification ................................................................................................... 112

5.4.2.2. Exposure assessment ................................................................................................... 113

5.4.2.3. Hazard characterisation ............................................................................................... 115

5.4.2.4. Risk characterisation .................................................................................................... 118

5.4.2.5. Limitations of the risk assessment and future recommendations .............................. 120

5.5. Conclusion ................................................................................................................................ 123

Chapter 6. General discussion and conclusions ..................................................................... 124

6.1. General discussion .................................................................................................................... 124

6.2. Conclusion ................................................................................................................................ 133

Bibliography ............................................................................................................................ 135

xii

List of Figures

Figure 1-1. Immuno-electron micrograph of NoV in stool samples (reproduced from Kapikian et al.

(1972)). ....................................................................................................................................... 5

Figure 1-2. Illustration of cryo-image reconstruction (A) and x-ray crystallography (B) of recombinant

Norwalk virus capsid structure; and three ribbon-protein domains (C) (reproduced from

Prasad et al. (1999)). ................................................................................................................... 7

Figure 1-3. The NoV genome (reproduced from Karst et al. (2014)). ..................................................... 8

Figure 1-4. Schematic representation of the NoV genome representing five regions frequently used

for detection and genotyping study (reproduced from Mattison et al. (2009)). ....................... 8

Figure 1-5. Shellfish from Bivalvia Class (reproduced from Gosling (2015)) ........................................ 11

Figure 1-6. Indonesia shellfish production from 2002-2011 (reproduced from FAO (2015)) .............. 13

Figure 1-7. The target sequences of ORF 1, ORF1-ORF2 junction and ORF2 for the detection of NoV GI

and GII genogroups (reproduced from Stals et al. (2012b)). .................................................... 16

Figure 2-1. Melt curve analysis of the standard and samples (A); and standard curve MS2 plasmid

from RT-qPCR assay generated from Rotor Gene 3000 (B) ...................................................... 38

Figure 2-2. The linear correlation between plaque assay and RT-qPCR on the quantification of

infectious MS2 .......................................................................................................................... 39

Figure 2-3. Comparison of RT-qPCR with no pre-treatment (■) and the plaque assay (▧) on the

quantification of infectious MS2 after heat treatment at 72°C (A) and chlorination with 0.5

ppm of ClO2 (B) with LOQ of RT-qPCR (―) and plaque assay (- -). ........................................... 40

Figure 2-4. Quantification of heat-inactivated MS2 with and without enzyme (RNase+RNasin, RNase

or TaqI) pre-treatment analysed by RT-qPCR(■) and plaque assay (▧) with LOQ of RT-qPCR

(―) and plaque assay (- -). ........................................................................................................ 41

xiii

Figure 2-5. MS2 inactivation by heat treatment at 72°C over 40 min as analysed by RT-qPCR without

(☐) or with RNase+RNasin pre-treatment () compared to the plaque assay () with LOQ of

RT-qPCR (―) and plaque assay (- -). ......................................................................................... 43

Figure 2-6. MS2 inactivation by exposure to different concentration of chlorine dioxide for 5 min at

25°C, analysed by RT-qPCR without (□) or with RNase+RNasin treatment (■) and plaque

assay (). ................................................................................................................................. 43

Figure 3-1. Acclimatisation and bioaccumulation process of Tasmanian Blue Mussel (Mytilus

galloprovincialis) ....................................................................................................................... 55

Figure 3-2. Thermal inactivation curves of NoV at 60 (A); 72 (B) and 90°C (C) in buffered media fitted

with Log linear-tailing (…), Weibull ( ̶ ̶ ̶ ) and Biphasic ( —) model. .................................... 63

Figure 3-3. Thermal inactivation curves of MS2 at 60 (A); 72 (B) and 90°C (C) in buffered media fitted

with Log linear-tailing (…), Weibull ( ̶ ̶ ̶ ), Weibull-tailing ( ̶ · ̶ ) and Biphasic ( —) model. 64

Figure 3-4. Thermal inactivation curves of NoV at 60 (A); 72 (B) and 90°C (C) in mussel matrix fitted

with Log linear-tailing (…), Weibull ( ̶ ̶ ̶ ) , Weibull-tailing ( ̶ · ̶ ) and Biphasic ( —) model.

.................................................................................................................................................. 65

Figure 3-5. Thermal inactivation curves of MS2 at 60 (A); 72 (B) and 90°C (C) in mussel matrix fitted

with Log linear-tailing (…), Log linear-shoulder-tailing (xxx), Weibull-tailing ( ̶ · ̶ ), Two-

mixed Weibull (═), Biphasic (—) and Biphasic-shoulder (○○○) model .............................. 66

Figure 3-6. Predicted general z curves in buffered media (—) and mussel matrix (…) of NoV (A) and

MS2 (B) in buffer (▲) and mussel matrix (□) at different temperatures. .............................. 70

Figure 3-7. Predicted specific z curves in buffered media (—) and mussel matrix (…)of NoV (A) and

MS2 (B) in buffer (▲) and mussel matrix (□) at different temperatures. .............................. 71

xiv

Figure 4-1. The observed () and predicted (---) values of ClO2 residue (C) (from (a) 10, (b) 20, and

(c) 40 ppm treatment at 20°C for different exposure times in buffered media. ..................... 84

Figure 4-2. The observed () and predicted (---) values of ClO2 residue (C) from (a) 10, (b) 20, and (c)

40 ppm treatment at 20°C for different exposure times in mussel matrix. ............................. 85

Figure 4-3. The log reductions (Log10(N/N0)) curves of NoV in the buffered media fitted using Hom

(…), Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆)ppm ClO2

for different exposure times ..................................................................................................... 87

Figure 4-4. The log reductions (Log10(N/N0)) curves of MS2 in the buffered media fitted using Hom

(…), Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆) ppm ClO2

for different exposure times ..................................................................................................... 87

Figure 4-5. The log reductions (Log10(N/N0)) curves of NoV in the mussel fitted using Hom (…),

Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆) ppm ClO2 for

different exposure times .......................................................................................................... 88

Figure 4-6. The log reductions (Log10(N/N0))curves of MS2 in the mussel fitted using Hom (…),

Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆) ppm ClO2 for

different exposure times .......................................................................................................... 89

Figure 5-1. Shellfish sampling locations in Jakarta and Panimbang ..................................................... 99

Figure 5-2. Shellfish species collected from Indonesian fish markets ................................................ 107

Figure 5-3. Phylogenetic tree of NoV GII detected from contaminated samples of Indonesian shellfish

................................................................................................................................................ 110

xv

List of Tables

Table 1-1. Standard quality for live shellfish and its processed products for direct consumption

(MMAF Indonesia, 2004) .......................................................................................................... 15

Table 1-2. Set of primer sequences for detection (D), genotyping (G) and quantification (Q)of NoV GI

and GII by RT PCR assay ............................................................................................................ 17

Table 3-1. Contact times of thermal inactivation at different temperatures. ...................................... 56

Table 3-2. The concentration of NoV and MS2 in seawater and mussel after bioaccumulation process

for 12 and 24 h.......................................................................................................................... 60

Table 3-3. The predicted time to log reduction at D, 2D and 4D and the calculated RMSE values from

the thermal inactivation curves of NoV in different matrices fitted by Log Linear, Weibull and

Biphasic models. ....................................................................................................................... 68

Table 3-4. The predicted time to log reduction at D, 2D and 4D and the calculated RMSE values from

the thermal inactivation curves of MS2 in different matrices fitted by Log Linear, Weibull and

Biphasic models. ....................................................................................................................... 68

Table 4-1. Exposure time of ClO2 treatment at different concentrations ............................................ 80

Table 4-2. The RMSE and R2 values of the ClO2 inactivation models of Hom, Weibull and Biphasic ... 86

Table 4-3. The average of observed maximum reduction of NoV and MS2 treated by ClO2 exposed for

certain periods .......................................................................................................................... 90

Table 5-1. The parameter utilised in the risk assessment to estimate the dose per serving, the

probability of illness and the number of NoV cases per year ................................................ 105

Table 5-2. The numbers of shellfish samples from Jakarta and Panimbang fish markets in 2016 and

2017 ........................................................................................................................................ 106

Table 5-3. The average extraction efficiency of MS2 as a control per batch...................................... 107

xvi

Table 5-4. NoV prevalence in the shellfish samples from Indonesian fish markets according to species

................................................................................................................................................ 108

Table 5-5. NoV prevalence in the shellfish samples from Indonesian fish markets according to

sampling sites ......................................................................................................................... 108

Table 5-6. NoV concentration in contaminated shellfish at traditional markets in Jakarta according to

species .................................................................................................................................... 109

Table 5-7. Assumptions on the proportion of shellfish cooked by different methods ....................... 115

Table 5-8. Input parameters for the deterministic QRA to estimate the risk of NoV in shellfish from

Indonesian fish markets.......................................................................................................... 117

Table 5-9. The NoV-illness cases per year estimated based on the assumption of the most common

shellfish cooking methods in Indonesia with the worst-cases scenario ................................. 119

Table 5-10. The estimated and reported attack rate of enteric virus due to shellfish consumption in

different scenario in one-year period ..................................................................................... 120

xvii

List of Abbreviations

ASP Amnesic Shellfish Poison

BIOHAZ Biological Hazard

Bps Base pairs

BSN Badan Standarisasi Nasional [National Standardization Agency of Indonesia]

CaCl2 Calcium Chloride

CFU Colony Forming Unit

ClO2 Chlorine dioxide

Ct value Cycle threshold value of real-time PCR

ddH2O Double-distilled water

DEPC Diethyl Pyrocarbonate

DT Digestive tissues

D value Time required to a log10 reduction (min)

E. coli Escherichia coli

EFSA European Food Safety Authority

FAO Food Agriculture Organization of United Nations

FAOSTAT FAO Statistic database

FCV Feline Calicivirus

GHP Good Handling Practices

GMP Good Manufacturing Practices

GII Genogroups II

HAV Hepatitis A Virus

HBGAs Histo-Blood Group Antigens

HEV Hepatitis E Virus

HPP High Pressure Processing

xviii

ISC RT-qPCR In Situ Capture Reverse Transcription Quantitative Polymerase Chain Reaction

Kb Kilobases

LAMP Loop-Mediated Isothermal Amplification

LB Lactose Broth

LTFC Long-Term Facilities Care

MIQE Minimum Information for publication of Quantitative Real-Time PCR Experiments

MMAF Ministry of Marine Affairs and Fisheries of Republic Indonesia

MNV Murine Norovirus

MPN Most Probable Number

MS2 MS2 bacteriophage

NoV Norovirus

ORF Open Reading Frames

P1 Protruding 1 domain of viral protein

P2 Protruding 2 domain of viral protein

PCR Polymerase Chain Reaction

PFU Plaque Forming Unit

PGM-MB Porcine Gastric Mucin-conjugated Magnetic Beads

PMA Propidium Monoazide

PMAxxTM Improved version of PMA by Biotium®

PSP Paralytic Shellfish Poison

P (value) Probability value or significance

PV Poliovirus

QMFSRA Quantitative Microbial Food Safety Risk Assessment

QMRA Quantitative Microbial Risk Assessment

R2 The coefficient of determination

xix

RdRp RNA-dependent RNA polymerase

RMSE Root Mean Square Error

RNA Ribonucleic Acid

RNase Ribonuclease

RNasin Ribonuclease Inhibitor

RT-qPCR Reverse Transcription Quantitative Polymerase Chain Reaction

S Shell domain of viral protein

SaV Sapovirus

SMV Snow Mountain Virus

SNI Standar Nasional Indonesia [Indonesian National Standard]

SSOP Sanitation Standard Operational Procedure

TaqI a restriction enzyme isolated from the bacterium Thermus aquaticus

TE Tris EDTA

TIA Tasmanian Institute of Agriculture

TuV Tulane Virus

UV Ultra Violet

VLPs Virus-like particles

VP1 Viral Protein of major capsid

VP2 Viral Protein of minor capsid

VPg Viral Protein genome-linked

WWF Word Wide Fund for Nature

z value Changes in temperature needed to produce 90% change in the reduction rate (D value)

xx

Abstract

Norovirus (NoV) infection is estimated to cause almost 20% of acute gastroenteritis cases

worldwide. Infants, the elderly and the immunocompromised are those most susceptible to NoV

infection. NoV is known to be persistent in the environment for long periods (60-80 days at 25°C), is

infectious at low doses (at 8 – 2,800 viral particles), can be shed at high concentration (up to 109-1011

viral copies per gram faeces of infected person), and is mainly transmitted through the faecal-oral

route. Therefore, a small amount of NoV contamination in the environment, water or food can cause

large outbreaks.

Shellfish, in particular, are susceptible to NoV contamination because they filter large amounts of

water and accumulate different types of suspended particles including bacteria and viruses when

grown or harvested from contaminated areas. In Indonesia, some shellfish growing and harvesting

areas are located close to estuaries which can be contaminated by untreated domestic sewage

effluent, especially during flood incidents. Even though shellfish in Indonesia are mostly consumed

cooked, inadequate cooking and cross-contamination during food preparation steps can lead to NoV

contamination in the prepared meal.

Risk assessment of NoV, especially in shellfish from Indonesian markets, remains challenging due to

the lack of prevalence data, no recorded NoV outbreaks caused by shellfish consumption, and the

lack of knowledge of the efficacy of post-processing steps including handling and cooking based on

consumer behaviour in Indonesia. Boiling, stir-frying and steaming are the most common cooking

practise of shellfish in Indonesia which can reduce the NoV contamination. In case the shellfish is

being consumed as a raw or fresh product, the use of disinfectant such as Chlorine Dioxide (ClO2) to

reduce the viral contamination or to prevent cross-contamination during post harvesting or handling

is a potential risk management strategy. In addition, standard quantification assays for NoV based on

the cell-culture system are as yet unavailable. Therefore, NoV studies rely on molecular based

methods such as Reverse Transcription Polymerase Chain Reaction (RT-PCR).

xxi

This project optimised a Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)

method to obtain prevalence data on NoV in shellfish from Indonesian markets and further utilised a

NoV surrogate (MS2 bacteriophage, ‘MS2’) for inactivation studies, to fill those data gaps. The

results provided better understanding of NoV prevalence and survival and could be used to predict

the risk of NoV contamination in shellfish from Indonesian markets.

The first aim of this thesis was to evaluate the application of RT-qPCR after pre-treatment with

enzymes because current methods quantify both infectious and non-infectious viral particles and

may over-estimate the risk of viral infections, especially in the inactivation and prevalence studies.

Therefore, sample pre-treatments are required to differentiate the infectious from non-infectious

viral RNA.

MS2, a cultivable NoV surrogate was used in this study. RT-qPCR after pre-treatment with RNase

followed by RNasin showed better performance than RNase alone or TaqI in the elimination of the

RNA from inactivated MS2 and produced a comparable result to the plaque assay. This modified RT-

qPCR method was shown to be applicable for the quantification of infectious MS2 after inactivation

treatment by heat or ClO2, producing comparable results to plaque assays.

The next aim of this thesis was to compare the inactivation kinetics of NoV and MS2 treated by

heating and ClO2 in buffered media (PBS solution) and the shellfish matrix, as the NoV surrogates

may have different inactivation kinetics compared to NoV. The efficacy of both inactivation methods

was also determined.

To provide artificial contamination of NoV and MS2 in the mussel for heat treatment studies,

bioaccumulation process of the viruses in Tasmanian Blue Mussel (Mytilus galloprovincialis) was

done to mimic the actual virus transmission routes in shellfish. While for ClO2 treatment studies, the

mussels were artificially contaminated by dipping the tissue in solutions of NoV and MS2 for 30 min

to represent the cross-contamination process. NoV and MS2 in buffered media and bioaccumulated

mussel were heated at 60, 72 and 90° C at various times. The evaluation of NoV and MS2

xxii

inactivation kinetics showed that the Weibull model performed better in estimating the survival of

NoV and MS2 in buffered media, while the Biphasic model provided better estimation of virus

survival in mussel matrix. The D values of NoV were generally higher than MS2 in both buffered

medium and mussel matrix, showing a higher resistance of NoV towards heat treatment.

Furthermore, for all temperatures, inactivation of both viruses in mussel matrix required a longer

time to achieve 1 log10 reduction compared to inactivation in buffered media.

The efficacy of chlorine dioxide (ClO2) to inactivate NoV and MS2 in buffered media and artificially-

contaminated mussel was studied using ClO2 at 10, 20 and 40 ppm with various exposure times at

25°C. The result showed that 40 ppm ClO2 treatment reduced NoV and MS2 more significantly in

both buffered media and mussel matrix than 10 and 20 ppm treatments. In general, the virus

reduction was higher in buffered media than in mussel matrix for all ClO2 treatments. For example,

the reduction of MS2 in buffered media treated with 40 ppm ClO2 for 200 min resulted in > 6 log10

PFU/ml reduction, while in mussel matrix the same treatment only reduced MS2 by < 3 log10 PFU/g.

At the same treatment, NoV in buffered media were reduced for more than 3 log10 copies/ml, while

only 2.36 log10 copies/g reduction was observed in mussel matrix. The inactivation of ClO2 of both

viruses in buffered media and mussel matrix was equally well described using the quasi-mechanistic

Hom model or the Weibull model.

The first prevalence data for NoV GII in shellfish in Indonesia are presented in this thesis. The data

are for three shellfish species i.e. Green Mussel (Perna viridis), Blood Cockle (Anadara granosa) and

Oriental Hard Clam (Meretrix lusoria), that are commonly consumed in Indonesia. Shellfish were

sampled from four fish markets in Jakarta and Panimbang, Indonesia, in July 2016 and 2017. The

NoV from extracted digestive tissue (DT) of shellfish was enumerated using the enzymatic pre-

treated RT-qPCR developed in this study. NoV GII was detected in 11 out of 171 samples with

contamination levels from 1.43 to 3.55 log10 copies/g DT. The NoV GII prevalence in Green Mussels

was 10%, which was higher than the prevalence in Oriental Hard Clam (7.14%) and Blood Cockle

xxiii

(2.9%). All NoV-contaminated shellfish were collected from traditional fish markets (Muara Kamal

and Cilincing) harvested from Jakarta Bay.

Due to the paucity of relevant data, a deterministic approach was used to estimate the risk of illness

due to the consumption of NoV contaminated shellfish from Indonesian markets. In the worst-case

scenario where the level of contamination is 8.98 x 103 log10 copies/g DT, boiling for more than 30

min during cooking step can significantly reduce the estimated NoV outbreaks due to shellfish

consumption.

Based on the results from the inactivation studies, both inactivation treatments (heat and ClO2) can

be used as control measures to reduce NoV contamination in shellfish. Even though MS2 was more

susceptible to heat treatment than NoV, the use of this surrogate in those studies has provided a

better understanding on inactivation kinetics and tailing phenomenon in both treatments. Together

with the data of NoV exposure or prevalence in shellfish from the markets, the result from the

inactivation studies was used to develop a risk assessment that can assist in risk management.

These data provided scientific evidence which can be applied to improve the quality and safety of

shellfish production and provide consumer protection from NoV infection in Indonesia. The findings

from this study also emphasised the need for regular surveillance in the polluted growing or

harvesting areas such as Jakarta Bay, and the application of proper cooking or disinfection to reduce

the risks of NoV gastroenteritis from consumption of the contaminated shellfish.

1

Chapter 1. Literature review

1.1. Introduction

Foodborne disease outbreaks cause serious health problems and are an economic burden in every

country. (WHO, 2013) estimated that 2.2 million people die each year due to foodborne and

waterborne outbreaks around the world. Many epidemiological studies of foodborne pathogens

have shown that bacteria and viruses have the potential to cause serious foodborne illness in

humans (Bartsch et al., 2016; Pires et al., 2015; Scallan et al., 2015). In the United States of America

they are responsible for 9.4 million episodes of foodborne illnesses per year (Scallan et al., 2011),

caused 112,000 DALYs (disability-adjusted life years) (Scallan et al., 2015) and associated with an

economic loss of US$10-83 billion per year (Nyachuba, 2010). Diarrhoea and vomiting are the most

noticeable symptoms caused by pathogenic foodborne microbes and potentially generate the

secondary transmission of the disease through faecal/fomites-oral route and person-to-person

transmission (Verhoef et al., 2015).

Among these causative agents, enteric viruses have been associated with high numbers of

gastroenteritis outbreaks in infants and the elderly especially at hospital, child care and long term

facilities care (LTFC) (Barclay et al., 2014; Bernard et al., 2014; Nic Fhogartaigh & Dance, 2013). Some

enteric viruses such as norovirus (NoV) and hepatitis A virus (HAV) have been found in aquatic

environments and thus contaminate shellfish (La Bella et al., 2016) and water used for food

processing and irrigation (Cook & Richards, 2013). These viruses can generate outbreaks as they can

be transmitted with relatively low ‘infectious dose’ through food or water to humans, or directly

from person-to-person (Atmar et al., 2014; Bitler et al., 2013; Hall et al., 2011). Enteric viruses are

commonly shed in high numbers in faeces and transferred to fomites in contact with the infected

patients, e.g.: NoV levels have been reported to range from 105 to 109 viruses/g faeces (Teunis et al.,

2015) and HAV up to 109 viruses/g faeces (Kotwal & Cannon, 2014; Tjon et al., 2006).

2

Although enteric viruses are mostly transmitted person-to-person, food and water are also potential

sources of contamination leading to many foodborne outbreaks. Various foods have been reported

to be contaminated by viruses and associated with outbreaks, including deli sandwiches (Daniels et

al., 2000), salad and produce (Gallimore et al., 2005; Mesquita & Nascimento, 2009; White et al.,

1986), raspberries (Le Guyader et al., 2004), frozen strawberries (Hutin et al., 1999), and shellfish

(Kohn et al., 1995; Le Guyader et al., 2006; Morse et al., 1986). Other studies also found that

contaminated water is responsible for many gastroenteritis outbreaks caused by enteric viruses

(Beller et al., 1997; Kukkula et al., 1999) indicating the use of contaminated water for irrigation,

aquaculture or drinking purposes. In Australia, outbreaks of HAV occurred in several states during

2009 caused by the consumption of semi dried tomatoes (Donnan et al., 2012), while in 2013 NoV

outbreaks were reported in Tasmania associated with the consumption of oysters (Lodo et al.,

2014).

Viruses have different structures and behaviours from bacteria. In general, viruses are more than 10

times smaller in size than bacteria with diameters ranging from 25 to 400 nm. Because of their small

size, most viruses cannot be observed under the light microscope. Viruses are unable to reproduce

and perform metabolic process without their host cell (i.e. specific cell type that they can infect and

in which they can proliferate). Most of them have a crystalline structure based on a protein shell

called a ‘capsid’ which encloses the DNA or RNA for replication and accessing the host cell (Madigan

et al., 2015; Panno, 2011; Prasad et al., 1999). Therefore, because of their relatively simple

structure, and particularly the absence of a membrane (i.e., ‘non-enveloped’ virus) some viruses

including human NoV, rotavirus and HAV are more resistant than bacteria from treatments such as

chlorination, UV and filtration during conventional wastewater treatment (Corrêa et al., 2012; Duizer

et al., 2004; Rzeżutka & Cook, 2004). Unlike the pathogenic bacteria, however, viruses are unable to

replicate themselves in the environment due to the lack of a host cell. Therefore, the number of

viruses will not increase after shedding from an infected individual and the public health risk will not

increase over time as the product moves through the supply chain.

3

This literature review introduces background information on epidemiology, biology, detection,

inactivation, and risk assessment of NoV in food. In addition, information about human enteric

viruses relevant to food and shellfish consumption is described to emphasize the importance of

human NoV in foods and foodborne outbreaks worldwide.

1.1.1. Human enteric viruses

Enteric viruses that are commonly associated with foodborne and waterborne outbreaks belong to

the families Adenoviridae (human adenoviruses serotype 40 and 41), Astroviridae (human astrovirus

types 1 to 8), Caliciviridae (NoV & sapoviruses), Picornaviridae (aichi viruses, enteroviruses and HAV),

Reoviridae (rotaviruses) (Bányai et al., 2018; Fong & Lipp, 2005; Le Guyader et al., 2008; Oude

Munnink & Van der Hoek, 2016; Thomas et al., 2013). Of these families, Caliciviridae, Picornaviridae

and Reoviridae are mostly found in faeces and fomites from infected people during gastroenteritis

outbreaks. Caliciviridae and Picornaviridae families have a similar morphology and structure, i.e.,

icosahedral, a non-enveloped RNA virus and similar genome configurations (King et al., 2011).

Enteric viruses contaminate food and water through two ways: i) inadequately treated human and

animal sewage that contaminates food and water environments and ii) direct contact of food and

water with a food handler who has infected by the virus (Gallimore et al., 2005; Maunula & Von

Bonsdorff, 2014; Tuladhar et al., 2013).

Numerous food and waterborne outbreaks have been caused by enteric viruses such as NoV, HAV,

hepatitis E (HEV), rotavirus, astrovirus and sapovirus (SAV). In USA, Scallan et al. (2011) estimated

that 59% (5.51 million of a total of 9.4 million) of cases of foodborne illnesses were caused by

viruses. Among these viruses, NoV has been estimated as the major cause of viral foodborne illness

in USA comprising at least 99% (5.46 million) of the cases, while other enteric viruses compose only

less than 1% from the total cases (Scallan et al., 2011). In addition, other studies have also reported

the contribution of enteric viruses to foodborne cases worldwide, such as NoV, aichiviruses,

rotaviruses, SaV, enteroviruses, astroviruses, and HEV, in Japan (Iritani et al., 2014; Miyashita et al.,

4

2012; Shibata et al., 2015; Usuku et al., 2008), SaV ini Puerto Rico (Hassan-Ríos et al., 2013), NoV,

rotaviruses and SaVs in Northern Arabian Gulf (Gallimore et al., 2005), NoV in Sweden, (Hedlund et

al., 2000), HAV in the Netherlands and Australia (Donnan et al., 2012; Fournet et al., 2012) and NoV,

rotaviruses, and HAV in the USA (Fletcher et al., 2000; Hutin et al., 1999; Noel et al., 1997).

In general, the numbers of viral foodborne cases caused by non-NoV are lower than NoV. This is

probably due to several reasons. Firstly, some enteric viruses remain unreported and not necessarily

diagnosed as causative of foodborne cases by general practitioners (Maunula & Von Bonsdorff,

2014). Secondly, the availability of vaccines for several enteric viruses such as rotavirus, HAV and

HEV may reduce or prevent outbreaks (Nelson et al., 2014; Van Herck et al., 2011; Yen et al., 2011).

Thirdly, some viruses such as rotavirus and adenoviruses are childhood disease (Amaral et al., 2015),

thus child vaccination program provides a sufficient host-immunity to the viral infection (Braeckman

et al., 2012). Lastly, NoV is also known to be persistent in the environment and has a low ‘infectious

dose’, at 18-2,800 viral particles (Rodríguez-Lázaro et al., 2012; Teunis et al., 2008). Combined with

high shedding rates of NoV from infected humans, a single infected individual has a potential to

transmit and infect hundreds of thousands of people (Pringle et al., 2015). Therefore, the low

‘infectious dose’ and high shedding rate are presumably the main reasons that NoV is the major

enteric virus associated with outbreaks.

As a consequence, NoV is an important issue to be addressed by food safety researchers and health

authorities in many countries. Many studies have assessed the risk for consuming food and water

contaminated by NoV such as produce (Barker, 2014; Bouwknegt et al., 2015; Laura et al., 2012;

Mok et al., 2014), shellfish (Croci et al., 2007; Suffredini et al., 2014) and drinking water (Masago et

al., 2006). Although the NoV can now be cultured in vivo using stem cell-derived from human

enteroids (Ettayebi et al., 2016), however, this cell culture system is still unsuitable as a robust

quantification assay for NoV (Ettayebi et al., 2016). Thus, it hampers the development of inactivation

models and risk assessment studies. Therefore the use of cultivable NoV surrogates for inactivation

studies such as murine norovirus (MNV) (Bozkurt et al., 2014b), feline calicivirus (FCV) (Buckow et

5

al., 2008), virus-like particles (VLPs) (Feng et al., 2011; Koromyslova et al., 2015) and MS2

bacteriophage (MS2) (Bae & Schwab, 2008; D'Souza & Su, 2010) could be alternatives even though

their genetic structures are different from human NoV.

1.1.2. Human norovirus

NoV, previously known as Norwalk-like virus (Figure 1-1), causes almost 20% of human

gastroenteritis outbreak cases worldwide (Ahmed et al., 2014; Karst et al., 2015). There are three

genogroups of NoV (GI, GII and GIV) associated with human gastroenteritis outbreaks (Karst et al.,

2015; Zheng et al., 2006). These genogroups are further divided into 33 genotypes based on amino

acid sequence diversity in the complete VP1 capsid protein, with 9 genotypes in GI, 22 genotypes in

GII and 2 genotypes in GIV (Vinjé, 2015). Of these, only GI and GII genogroups, known as human

NoV, are frequently found as contaminants in food and have caused human gastroenteritis through

the faecal-oral route (Scallan et al., 2011; Torok, 2013; Yu et al., 2015), especially in raw or uncooked

shellfish (Li et al., 2014).

Figure 1-1. Immuno-electron micrograph of NoV in stool samples (reproduced from Kapikian et al. (1972)).

Each NoV genogroup has been reported to be specific with respect to binding capability to the host

(Tan & Jiang, 2007), environmental persistence (Seitz et al., 2011; Verhaelen et al., 2013) and

removal or elimination responses (Cook et al., 2016; da Silva et al., 2007). These differences may

influence the epidemiological patterns (Matthews et al., 2012), the distribution in the environment

(Hoa et al., 2013) and transmission to the host, especially to humans (Vega et al., 2014). For

6

example, in a profiling study of NoV genogroups and genotypes during outbreaks, Verhoef et al.

(2010) showed that although NoV GII was also involed in some foodborne and waterborne

outbreaks, NoV GI was more likely to be associated with foodborne cases while NoV Genogroup II

including genotype 4 (GII.4) strains were more often related to person-to-person outbreaks.

Therefore, the proportion of NoV genotypes associated with foodborne outbreaks could be

estimated by analysing NoV outbreak data and genotype profiling from different outbreaks globally

(Verhoef et al., 2015).

1.1.3. Structure and biology of norovirus

Human NoV is a small virus, with 23-40 nm in diameter and classified in the family Caliciviridae

(Vinjé, 2015). NoVs are non-enveloped with icosahedral symmetry composed of 180 protein

molecules that form the capsid. The molecules are organised into 90 dimers which have three basic

domains, i.e., S, P1 and P2 (Estes et al., 2006). These domains are linked by a flexible hinge. This

morphological structure of NoV has been illustrated from the study of three-dimensional structure

of recombinant Norwalk virus capsid by Prasad et al. (1999) using cryo-image reconstruction and x-

ray crystallography (Figure 1-2).

7

[A] [B]

[C]

Figure 1-2. Illustration of cryo-image reconstruction (A) and x-ray crystallography (B) of recombinant Norwalk virus capsid structure; and three ribbon-protein domains (C) (reproduced from Prasad et al.

(1999)).

The genome of human NoV is composed of single-stranded, positive-sense RNA of approximately 7.6

kb length and containing 3 open reading frames (ORFs): ORF1, ORF2 and ORF3 (Atmar et al., 2018).

The ORF1 is translated to encode a polyprotein containing six to seven non-structural proteins,

including the VPg and the viral RNA-dependent RNA polymerase (RdRp), while the ORF2 and ORF3

are translated from sub-genomic RNA to form two structural proteins during viral replication, the

major (VP1) and the minor (VP2) capsid (Karst et al., 2014; Karst et al., 2015; Thorne & Goodfellow,

2014) (Figure 1-3). Generally, the genetic diversity of human NoV is determined from the variability

of RdRp and VP1 gene (Kroneman et al., 2013; Stals et al., 2012a; Vinjé et al., 2004; Zheng et al.,

2006).

8

Figure 1-3. The NoV genome (reproduced from Karst et al. (2014)).

The ORF1 and ORF2 sequences contain five genomic regions (A, B, C, D and E) that have become the

most interesting sequences for detection and genotyping studies (Kroneman et al., 2013; Stals et al.,

2012b). These five genomic regions are considered as the most conserved region for GI and GII

genogroups (Jothikumar et al., 2005; Kageyama et al., 2003; Loisy et al., 2005; Vinjé et al., 2004), and

are widely used for NoV genotyping purpose following single and dual-nomenclature system

(Kroneman et al., 2013). Among these, the B and C regions are now commonly used for detection of

NoV than theother regions (Le Guyader et al., 2009; Trujillo et al., 2006; Vinjé, 2015). The A and B

regions are located at the ORF1 encoding RNA polymerase/RdRp, while region C, D and E are located

at the ORF1-ORF2 junction and ORF2 encoding VP1 capsid protein, (Figure 1-4) (Mattison et al.,

2009).

Figure 1-4. Schematic representation of the NoV genome representing five regions frequently used for detection and genotyping study (reproduced from Mattison et al. (2009)).

9

1.1.4. Foodborne norovirus related diseases

A comprehensive study of NoV epidemiology from 1999-2012 by Verhoef et al. (2015) reported that

person-to-person transmission is the main source of NoV outbreaks and almost 14% of all NoV

outbreaks are associated with food as a source of exposure, while the other sources are water and

environment. GII.4 was the major causative genotype of NoV outbreaks worldwide being responsible

for at least 62% of total NoV cases (Siebenga et al., 2009). This is probably due to the emergence of

new variant GII.4 strains every year replacing the previous dominant strains of NoV GII.4 (not other

endemic strains) (Siebenga et al., 2009). The high mutation frequency of this strain enhances their

ability to bind a wider range of histo-blood group antigens (HBGAs) (White, 2014).

Based on its rapid evolution and immunogenetic response, GII.4 viruses are able to cause

gastroenteritis outbreaks in susceptible populations through person-to-person and environmental

transmission (Eden et al., 2013; Lindesmith et al., 2012). Non-GII.4 genotypes such as GI.3, GI.6, GI.7,

GII.3, GII.6, and GII.12 are more resistant to mutation and only cause gastroenteritis outbreaks via

food and water transmission route (Vega et al., 2014; White, 2014). Accordingly, several studies

have suggested that these genotypes were more consistently the causative agents of waterborne

and foodborne outbreaks rather than person-to-person route (Matthews et al., 2012; Vega et al.,

2014; Verhoef et al., 2010).

Among the various types of food, produce and shellfish are more susceptible to NoV contamination.

Many studies reported that NoV outbreaks were associated with the consumption of contaminated

ready-to-eat food such as oyster, clam (Huppatz et al., 2008; Lodo et al., 2014; Morse et al., 1986;

Westrell et al., 2010) and fresh produce (Daniels et al., 2000; Gallimore et al., 2005; Mesquita &

Nascimento, 2009; Rajko-Nenow et al., 2014). These foods have been indicated to sometimes be

grown in, irrigated with and/or processed with NoV-contaminated water, and because they are

usually eaten without a proper cooking step, these represent a potential route of human exposure

to NoV.

10

1.1.5. NoV in shellfish

NoVs have been reported to be introduced to water environment by the sewage overflows

(Rodríguez et al., 2012) and contaminated marine water (Wyn-Jones et al., 2011; Yang et al., 2012),

urban catchments water and estuarine bay (Aw et al., 2009). Due to the presence and persistence of

NoV in the water (Cook et al., 2016), shellfish, as a filter feeder animal, are more susceptible to

contamination than other seafood products (Lees, 2000). NoV contamination in shellfish has been

reported from markets worldwide, such as France (Loutreul et al., 2014), Thailand (Kittigul et al.,

2016), Italy (Terio et al., 2010) and Australia (Symes et al., 2007). Other studies have also reported

the presence of NoV in shellfish harvested from Portugal (Mesquita et al., 2011), UK (Lowther et al.,

2012), Italy (Croci et al., 2007), France (Le Guyader et al., 2009), the Netherlands (Boxman et al.,

2006), Australia (Brake et al., 2014), Japan (Maekawa et al., 2007) and India (Umesha et al., 2008).

Although the contamination has been widely reported, the risk assessment of NoV in shellfish is still

rare and partially performed, especially in Asian countries. In Indonesia particularly, the NoV

prevalence in shellfish from Indonesian fish markets or harvesting area is not yet available.

Consequently, acquiring knowledge for risk assessment of NoV in shellfish has become important to

provide better understanding of NoV outbreaks worldwide including in Indonesia to aid the

development of preventive strategies against future outbreaks.

1.2. Bivalve molluscan shellfish

1.2.1. Biology of shellfish

Bivalve molluscs are soft bodied animals that belong to the Bivalve class. The soft bodies are

protected by two opposed shell valves composed of calcium carbonate. This class is the second

largest class within the molluscs and consists of 7,500 species. Generally, species identification of

bivalves is based on their colour, shape and marking on the shell. More than 80% of bivalves live in

the ocean and these organisms are important element of marine and freshwater habitats (Gosling,

11

2003, 2015). Some of these bivalves including mussels, oysters, scallops and clams (Figure 1-5) are

also called as ‘shellfish’ in aquaculture and fishery studies.

Figure 1-5. Shellfish from Bivalvia Class (reproduced from Gosling (2015))

Shellfish are highly modified molluscs, including modification of the gill function to entrap food

particles from the aqueous environment. It enables shellfish to feed efficiently in aqueous

environments. This feeding system, known as ‘filter feeding’, is the most efficient system of ciliary

feeding in sea animals (Gosling, 2003). Shellfish are able to filter large volumes of water from their

environment and accumulate different types of suspended food particles, and pathogenic bacteria

and viruses (Le Guyader et al., 2013; Lees, 2000), in their gills. Moreover, these accumulated viruses

are concentrated in DT by HBGA-like for carbohydrate ligand molecules which may enhance the

bioaccumulation process (Maalouf et al., 2011). Hence many studies have proposed that DT can be

used for detection, quantification and isolation of NoV from shellfish.

1.2.2. Shellfish production

There are five major groups of bivalve molluscs which are commonly consumed by humans and

grown/harvested and sold commercially: mussels, oysters, scallops, clams and cockles. In 2010,

12

world shellfish production was 10% of the total global fisheries production, with 14.6 million tons of

production. 12.9 million ton of this production originated from aquaculture activities, consisting of

38% clams, cockles and ark shells; 35% oysters; 14% mussels and 13% scallops. The high demand for

shellfish in the global market, at US$ 2.1 billion in 2009, triggered high production of shellfish

worldwide. Scallops were the most important shellfish species in international markets and

accounted for 46% of the total shellfish production (Karunasagar, 2014). However, the increasing

scale of shellfish production should be matched by increasing the public awareness about the risk of

raw shellfish consumption.

1.2.3. Shellfish in Indonesia

As shown in Figure 1-6, a statistical report from Food Agriculture Organization (FAO) (2015) showed

that, during the period 2007 to 2010, shellfish production in Indonesia increased rapidly from 10,000

to 70,000 metric tonnes. In 2011, the proportion of shellfish consumption was only around 15% of

total shellfish production, while the rest was utilised for non-consumption purposes such as pearl

oyster. Up to 2007 the trend of shellfish consumption in Indonesia was relatively stable with the

average of 10,000 metric tonnes per year (FAO, 2015). Shellfish consumed in Indonesia are mainly

produced from aquaculture activities including fresh, brackish and marine water (Nurdjana, 2006).

The major commodities are Green Mussel (Perna viridis), Oriental Hard Clams (Meretrix lusiora),

Bamboo Clam (Ensis directus), Blood Cockle (Anadara granosa), and Feather Cockle (Anadara

antiquata).

13

Figure 1-6. Indonesia shellfish production from 2002-2011 (reproduced from FAO (2015))

People in Indonesia usually consume cooked shellfish, such as boiled, steamed or fried. Eating raw

shellfish, such as oysters, has not been widely introduced. It has started to be advertised in restaurants

in several big cities such as Jakarta, Surabaya and Denpasar, especially for tourists or ‘foreigners’. In

this case, the oyster’s quality is strictly controlled, and the raw materials are mostly imported from

Eastern Asian countries such as Taiwan, Korea and Japan that employ shellfish sanitation programs.

Decree of the Indonesian Minister of MAF no. KEP.17/MEN/2004 regulates the Indonesian shellfish

sanitation system and aims to ensure the production of safe shellfish from Indonesia for local and

export markets. The regulation assists different parties that play roles in the shellfish production

system, including the shellfish farmers, processors, distributors, and the competent authorities who

monitor and control the application of sanitation system. The central and local competent

authorities are responsible for conducting monitoring and routine surveillance on the application of

the Sanitation Standard Operational Procedure (SSOP), Good Manufacturing Practices (GMP), Good

Hygienic Practices (GHP), as well as the integrated quality management program based on HACCP, in

every aspect of the shellfish production system. The authorities are also responsible for laboratory

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Prod

uctio

n (m

etrix

ton)

Year

Consumption

Non Consumption

14

testing to ensure the shellfish conformity with safety and quality requirements set in the Indonesian

National Standard (SNI) No. 3460.1; 3460.2; and 3460.3 (BSN, 2009).

The sanitation system also includes regulation for the shellfish farms across Indonesian waters. The

farm’s locations are regularly assessed, then the water qualities are recorded and routinely

monitored to determine the suitability of the locations to be used to grow the shellfish. Based on the

microbiological quality of the water and the possibility of pollution in the area due to the natural

cause and anthropogenic activities, the shellfish growing areas are classified into permissible areas,

permissible areas with certain condition, limited areas and off-limit areas. Shellfish farming activities

are prohibited in the off-limit areas. These areas are characterised by a high level of faecal

contamination, an exceeding level of PSP toxin, or the areas that have not been assessed for the

sanitation compliance.

Another part of the Indonesian shellfish sanitary system relates with the post-processing activities,

such as handling, collection, processing and distribution. For live shellfish, the transportation and

distribution should be done in a temperature-controlled vehicle, to avoid the shellfish quality loss

and their survival. Furthermore, a repeat circulation system with sterilised water may be used for

depuration purposes. The standard quality and safety requirements set in the decree for live

shellfish and its processed products for direct consumption are presented in Table 1-1.

15

Table 1-1. Standard quality for live shellfish and its processed products for direct consumption (MMAF Indonesia, 2004)

Parameters Requirement Method of analysis Visual characteristics Eggshells clean from manure, giving reaction

to knock, contain normal intravulval liquid Visual observation

Faecal coliform/Eschericia coli

Coliform < 300 MPN/100 g and E. coli < 230 MPN/100 g of shellfish meat, based on 5 tubes

Most Probable Number (3 dilutions)

Salmonella Absence in 25 g of shellfish meat

Total PSP content Must not exceed 80 µg/100 g of shellfish meat Bioassay test

PSP (diarrhetic shellfish poisoning)

Negative Bioassay test

ASP (amnestic shellfish poisoning)

Must not exceed 20 µg/100 g of domoic acid HPLC

Mercury (Hg) Must not exceed 0.5 mg/kg Lead (Pb) Maximum of 1.5 mg/kg Cadmium (Cd) Maximum of 1 mg/kg

1.3. Detection and quantification methods for noroviruses

To improve the safety of shellfish in the European countries, the European Food Safety Authority

(EFSA) has published a scientific opinion that contains recommendations to the European Council for

the establishment of regulations to control NoV contamination in oysters. One of the

recommendations is to investigate the levels of NoV contamination in shellfish which requires a

suitable method of identification and quantification (EFSA Panel on Biological Hazards (BIOHAZ),

2012). Accordingly, many studies have detected and quantified NoV in shellfish, in water as well as

sewage using methods such as conventional RT-PCR (Baert et al., 2007; Kageyama et al., 2003;

Kojima et al., 2002; Vinjé et al., 2004), RT-qPCR (Greening & Hewitt, 2008; Le Guyader et al., 2009;

Suffredini et al., 2014), enzyme-based colorimetric assay (Batule et al., 2018), immunoassay and

LAMP.

Among these methods, RT-qPCR has become a gold standard assay for both detection and

quantification (ISO, 2013; ISO, 2017), and it is widely used in NoV quantification studies (Kirby &

Iturriza-Gómara, 2012; Le Guyader et al., 2006; Vinjé, 2015). However, RT-qPCR may fail to

16

distinguish between infectious and non-infectious viruses in the sample because the assay will

quantify the RNA from both infectious and non-infectious viral particles. This drawback can lead to

misinterpretation of viral inactivation data for food quality control (Ceuppens et al., 2014). As a

consequence, sample pre-treatments to differentiate infectious from non-infectious viral RNA and

modification of RT-qPCR methods are required to provide a better analysis.

1.3.1. Primer sequences for detection, genotyping and quantification of NoV by RT-qPCR

Since the beginning of 2000’s, the use of both conventional and RT-qPCR methods to detect NoV has

increased rapidly. Many highly sensitive primer sets have been designed to detect both NoV GI and

GII such as in food, environmental and clinical samples as shown in Table 1-2. Most of the primers

target the sequences of ORF1, ORF1-ORF2 junction and ORF2 (GenBank accession no. X86557, nt

4997 to 5108) for GII detection, and sequences from the ORF1-ORF2 junction and ORF2 (GenBank

accession no. M87661, nt 5271 to 5385) for GI detection, (Figure 1-7), and only few primers target

different sequences of ORF2 in region D of GI (nt 5354 to 6914) and GII (nt 6432 to 6684) (Kong et

al., 2015; Vinjé et al., 2004).

Figure 1-7. The target sequences of ORF 1, ORF1-ORF2 junction and ORF2 for the detection of NoV GI

and GII genogroups (reproduced from Stals et al. (2012b)).

17

Table 1-2. Set of primer sequences for detection (D), genotyping (G) and quantification (Q)of NoV GI and GII by RT PCR assay

NoV Geno-group

Primer Sequences Polar-ity

Melting Temp (°C)

Product Length

(bp) Location

Type of assay*

(D/G/Q)1,2 References

GI G1SKF 5'-CTG CCC GAA TTY GTA AAT GA-3' + 49.7 329 ORF1-ORF2 junction & ORF 2 D/G1,2 (Kojima et al., 2002)

G1SKR 5'-CCA ACC CAR CCA TTR TAC A-3' - 51.1 329 ORF1-ORF2 junction & ORF 2 D/G1,2 (Kojima et al., 2002)

COG1F 5'-CGY TGG ATG CGN TTY CAT GA-3' + 55.9 84 ORF1-ORF2 junction Q1,2 (Kageyama et al., 2003)

COG1R 5'-CTT AGA CGC CAT CAT CAT TYA C-3' - 53 84 ORF1-ORF2 junction Q1,2 (Kageyama et al., 2003)

G1FFa 5'-ATH GAA CGY CAA ATY TTC TGG AC-3' + 55.3 596 ORF1-ORF2 junction & ORF 2 G1,2 (Kageyama et al., 2004)

G1FFb 5'-ATH GAA AGA CAA ATC TAC TGG AC-3' + 51.7 596 ORF1-ORF2 junction & ORF 2 G1,2 (Kageyama et al., 2004)

G1FFc 5'-ATH GAR AGR CAR CTN TGG TGG AC-3' + 60.6 596 ORF1-ORF2 junction & ORF 2 G1,2 (Kageyama et al., 2004)

G1SKR 5'-CCA ACC CAR CCA TTR TAC A-3' - 51.1 596 ORF1-ORF2 junction & ORF 2 G1,2 (Kageyama et al., 2004)

Cap A 5'-GGC WGT TCC CAC AGG CTT-3' + 54.2 177 ORF2 G1 (Vinjé et al., 2004)

Cap B1 5'-TAT GTT GAC CCT GAT AC-3' - 57.6 177 ORF2 G1 (Vinjé et al., 2004)

Cap B2 5'-TAT GTI GAY CCW GAC AC-3' - 59.1 177 ORF2 G1 (Vinjé et al., 2004)

NIFG1F 5'-ATG TTC CGC TGG ATG CG-3' + 55.9 92 ORF1-ORF2 junction Q1,2 (Miura et al., 2013)

QNIF4 5'-CGC TGG TAG CGN TTC CAT-3' + 55 86 ORF1-ORF2 junction Q1,2 (da Silva et al., 2007)

NV1LCR 5'-CCT TAG ACG CCA TCA TCA TTT AC-3' - 56 86 ORF1-ORF2 junction Q1,2 (Svraka et al., 2007)

NKIF 5'-GTA AAT GAT GAT GGC GTC TAA-3' + 50.3 305-314 ORF2 D/G1 (Kong et al., 2015)

NKI-F2 5'-GAT GGC GTC TAA GGA CGC-3' + 55.8 305-314 ORF2 D/G1 (Kong et al., 2015)

NKIR 5'-ACC CAD CCA TTR TAC ATY TG-3' - 50.8 305-314 ORF2 D/G1 (Kong et al., 2015)

MON 432 5’-TGG ACI CGY GGI CCY AAY CA-3’ + 57.2 213 ORF1 D/G1,2 (Richards et al., 2004)

MON 434 5’GAA SCG CAT CCA RCG GAA CAT-3’ - 56.3 213 ORF1 D/G1,2 (Morillo et al., 2012)

18

Table 1-2. Continued… NoV

Geno-group

Primer Sequences Polar-ity

Melting Temp (°C)

Product Length (bp) Location

Type of assay*

(D/G/Q)1,2 References

GII G2SKF 5'-CNT GGG AGG GCG ATC GCA A-3' + 57.6 343 ORF1 & ORF2 D/G1,2 (Kojima et al., 2002)

G2SKR 5'-CCR CCN GCA TRH CCR TTR TAC AT-3' - 62.4 343 ORF1 & ORF2 D/G1,2 (Kojima et al., 2002)

G2FBa 5'-GGH CCM BMD TTY TAC AGC AA-3' + 57.9 479 ORF1 & ORF2 Q1,2 (Kageyama et al., 2004)

G2FBb 5'-GGH CCM BMD TTY TAC AAG AA-3' + 55.9 479 ORF1 & ORF2 Q1,2 (Kageyama et al., 2004)

G2FBc 5'-GGH CCM BMD TTY TAC ARN AA-3' + 57.9 479 ORF1 & ORF2 Q1,2 (Kageyama et al., 2004)

G2SKR 5'-CCR CCN GCA TRH CCR TTR TAC AT-3' - 62.4 479 ORF1 & ORF2 D/G1,2 (Kageyama et al., 2003;

Kojima et al., 2002)

Cap C 5'-CCT TYC CAK WTC CCA YGG-3' + 54.2 253 ORF2 G1 (Vinjé et al., 2004)

Cap D1 5'-TGT CTR STC CCC CAG GAA TG-3' - 57.6 253 ORF2 G1 (Vinjé et al., 2004)

Cap D3 5'-TGY CTY ITI CCH CAR GAA TGG-3' - 59.1 253 ORF2 G1 (Vinjé et al., 2004)

COG2F 5'-CAR GAR BCN ATG TTY AGR TGG ATG AG-3' + 57.6 97 ORF1-ORF2 junction Q1,2 (Kageyama et al., 2003)

QNIF2 5'-ATG TTC AGR TGG ATG AGR TTC TCW GA-3' + 57.4 88 ORF1-ORF2 junction Q1,2 (Loisy et al., 2005)

JJV2F 5'-CAA GAG TCA ATG TTT AGG TGG ATG AG-3' + 55.6 97 ORF1-ORF2 junction D/Q1,2 (Boxman et al., 2009;

Jothikumar et al., 2005)

COG2R 5'-TCG ACG CCA TCT TCA TTC ACA-3' - 56.6 97 ORF1-ORF2 junction D/Q1,2 (Jothikumar et al., 2005;

Kageyama et al., 2003)

NVG2flux1 5'-ATG TTY AGR TGG ATG AGR TTY TC-3' + 55.3 88 ORF1-ORF2 junction Q1 (Nordgren et al., 2008)

NVG2flux2 5'-GGG AGG GCG ATC GCA ATC T-3' + 55.4 51 ORF1-ORF2 junction Q1 (Bucardo et al., 2017)

MON 431 5’-TGG ACI AGR GGI CCY AAY CA-3’ + 54.9 213 ORF1 D/G1,2 (Richards et al., 2004)

MON 433 5’GAA YCT CAT CCA YCT GAA CAT-3’ - 52.4 213 ORF1 D/G1,2 (Morillo et al., 2012)

Note: * (D=detection; G=genotyping; Q=quantification assay) 1 Primer has been used in the PCR assay for clinical samples 2 Primer has been used in the PCR assay for various food matrices including shellfish samples

19

The ORF1 and ORF1-ORF2 junction encoding both RdRp and major capsid (V1) is a sufficiently

conserved region for NoV GI and GII detection (Kageyama et al., 2003; Stals et al., 2012b). Therefore,

the use of primers designed from these sequences were able to detect 95-99% of GII genogroups

from confirmed positive samples with sensitivity from <10 to 104 genomic copies (Kojima et al.,

2002; Vinjé et al., 2004) but in some cases, these primers are less able to detect emerging variants of

GII.4 genotypes (Stals et al., 2012b). To improve the detection of these new variants there is a need

to design or develop new primer sets from different sequence regions based on new strains isolated

and identified from new outbreaks.

Based on the reverse transcription reaction prior to PCR assay, there are two types of RT-qPCR

method, i.e., one and two-step RT-qPCR. Both methods are comparable in terms of specificity,

efficiency and reliability. Although, two-step RT-qPCR has been commonly applied in NoV

quantification for clinical and food samples, the one-step method could be a promising method for

routine analysis because it is quicker, easier, and less expensive (Al-Shanti et al., 2009; Kirby &

Iturriza-Gómara, 2012). In addition, the use of a single reaction tube in the one-step method could

minimise sample cross-contamination, and the consequences of inaccurate pipetting during the

reverse transcription (Hanaki et al., 2014; Vinjé, 2015). Therefore, one-step RT-qPCR has been

applied for standard quantification of NoV (ISO, 2013; ISO, 2017), such as in inactivation and

surrogate studies in shellfish, water and faecal samples (Coudray-Meunier et al., 2015; Fuentes et

al., 2014; Jothikumar et al., 2005; Miura et al., 2013).

A method which can detect multiple NoV genogroups (GI, GII and GIV) simultaneously has been

developed using multiplex RT-qPCR and it has been suggested to be useful for the rapid screening of

NoV in food and water (Miura et al., 2013). However, single RT-qPCR produces a better sensitivity

than multiplex, especially when detecting low numbers of NoV, probably due to the use of more

probes (Niwa et al., 2014) and the number of genomes to be amplified (Stals et al., 2009) by

multiplex RT-qPCR. Furthermore, single RT-qPCR is more suitable to be used than multiplex assays in

inactivation studies where only one NoV genogroups is being studied at a time.

20

1.3.2. Sample pre-treatment in NoV inactivation studies to differentiate infectious/non-

infectious viruses

Published risk assessment studies on NoV in produce and ready-to-eat food (Bouwknegt et al., 2015;

Mokhtari & Jaykus, 2009; Stals et al., 2015), drinking water and environment (Masago et al., 2006;

Mok et al., 2014; Victoria et al., 2010) and shellfish products (Ventrone et al., 2013) have quantified

both infectious and non-infectious viruses without differentiation. Since only infectious particles of

NoV can infect humans, the number of quantified viral particles in those studies might not represent

the amount of infectious virus in the samples.

In recent years, the application of a pre-treatment step prior to RT-qPCR assay has been widely

studied to quantify infectious norovirus (Gyawali et al., 2019; Knight et al., 2012). The infectivity of

virus can be determined by its genom stability or capsid integrity (Knight et al., 2012). Thus, the

mechanism of pre-treatment step is based on two different processes, i.e., the capability of the

specific substances and chemical to disrupt the genom of infectious viral particles (damaged capsid),

or to bind the infectious viral particles (undamaged capsid and genom) (Knight et al., 2012).

Pre-treatments with photoactivable dyes (propidium monoazide (PMA), PMAxxTM, PEMAXTM and

EMA) (Gyawali & Hewitt, 2018; Kim & Ko, 2012; Oristo et al., 2018; Parshionikar et al., 2010), Porcine

Gastric Mucin (PGM) (Li et al., 2013; Ye et al., 2014), in situ capture (Wang et al., 2014) and RNase

(Richards et al., 2012; Ronnqvist et al., 2013) prior to RT-qPCR assay have been applied to evaluate

the efficacy of NoV inactivation treatment by quantifying the infectious NoV. The use of RT-qPCR

pre-treated with RNase are, to date, the most reliable and promising methods to be applied because

they are more efficient and economically affordable than the other methods.

RNase is known to be effective as a pre-treatment to quantify infectious viral particles of NoV

surrogates such as MNV, MS2 and HAV (Nuanualsuwan & Cliver, 2003; Rodríguez et al., 2009). It is

able to distinguish infectious and non-infectious viral particles through the evaluation of capsid and

cell membrane integrity during nucleic acid extraction prior to RT-qPCR assay (Soto-Munoz et al.,

21

2014; Yang & Griffiths, 2014). The basic principle of this pre-treatment is the degradation of RNA

from inactive bacteria and non-infectious viruses which lack of cell membrane or viral capsid

integrity, respectively (Knight et al., 2012).

However, the efficacy of RNase to degrade viral RNA depends on the different inactivation methods

and target viruses in the assay. For example, RNase was more effective when used as a pre-

treatment for measuring infectious viral particles treated by UV than high temperature due to

different in the mechanism of genomic structure degradation by the different treatments

(Bhattacharya et al., 2004). In addition, an inactivation study of human NoV which is previously

known as a snow mountain virus (SMV), reported that RNase is less effective than PMA (Escudero-

Abarca et al., 2014). Each virus has different capsid structure and ionic strength, thus they have

different capabilities to survive changes in temperature, pH and ionic strength in the suspension

during inactivation experiments (Knight et al., 2012).

Another alternative enzyme group that has potential to be used as pre-treatment are restriction

enzymes (REs). Molloy and Symons (1980) reported the ability of eight REs to cleave DNA in an RNA-

DNA substrate and amongst them, HaeIII and TaqI have also been shown to cleave the RNA strand of

this heteroduplex substrate. A further study by Murray et al. (2010) identified the cut-site or

sequence-specific site of TaqI enzyme to cleave DNA and RNA strands as T/CGA, while other REs also

identified to have similar ability were AvaII (cut site G/GWCC, W=A or T), AvrII (cut site C/CTAGG)

and BanI (cut site G/GYRCC, Y=C or T, R=A or G). That study also showed that these enzymes cleave

RNA-DNA and DNA-DNA substrate at the same phosphodiester bonds. However, the efficiency of

these enzymes to hydrolyse RNA strands from heteroduplex substrates is at least two orders of

magnitude less than the hydrolysis of DNA from homoduplex (DNA-DNA) substrate. Despite this,

there is the potential for using TaqI as a pre-treatment enzyme prior to RNA extraction to eliminate

free RNA from non-infectious viruses. Study to evaluate the efficacy of this enzyme as a pre-

treatment prior RT-qPCR is necessary, especially for the viral quantification in water, faecal, food

matrices including shellfish as there is no available data about its efficacy until now.

22

1.4. Inactivation of human NoV in shellfish

Apart from the NoV quantification method development that has been described above, many

studies have also investigated the treatment to reduce or eliminate NoV using depuration (Polo et

al., 2014), high pressure processing (HPP) (Ye et al., 2014), high temperature (Ahmed et al., 2013;

Escudero-Abarca et al., 2014; Ettayebi et al., 2016; Li et al., 2013; Wang & Tian, 2014), electron

beam and gamma irradiation (Feng et al., 2011; Praveen et al., 2013), 70% ethanol, UV, chlorine and

other chemical sanitisers (Belliot et al., 2008; Costantini et al., 2018; D'Souza & Su, 2010; Ronnqvist

et al., 2013). Some of these treatments, such as UV and application of disinfectants might be

ineffective to eliminate viral particles bioaccumulated inside the shellfish because the treatments

cannot penetrate the viral particles inside the tissue. High temperature is considered as the best

treatments which resulted in a higher log reduction of viral particles either in artificially

contaminated shellfish or in buffered media (Araud et al., 2016; Bozkurt et al., 2014b; Kingsley et al.,

2014). In addition, chlorine-based compounds also caused high reduction of infectious viral particles

(D'Souza & Su, 2010), thus it can be applied as a potential disinfectant or sanitizer agent to reduce

viral particles which contaminated food by cross-contamination during processing and handling (FAO

& WHO, 2009). Since shellfish in Indonesia is commonly consumed in a cooked form, the application

of high temperature treatment may not affect the consumer preference. During post-harvest step,

the retailers in Indonesian generally wash the tissue or the whole body of shellfish using clean water

or water containing disinfectant (WWF-Indonesia, 2015). Therefore, high temperature treatment

and chlorine-based disinfectants could be the most effective way to reduce and to eliminate NoV in

naturally-contaminated and in cross-contaminated shellfish from Indonesian fish markets,

respectively.

1.4.1. NoV inactivation studies using surrogates

Despite the significant impact of NoV in foodborne disease, the major limitation to the study of this

virus is its uncultivable nature (Cannon et al., 2006; Patel et al., 2008). To overcome this, some

23

studies on NoV inactivation have proposed the use of a cultivable NoV surrogate such as FCV,

murine noroviruses (MNV-1), tulane virus (TuV) or MS2 (Cromeans et al., 2014; Farkas et al., 2010;

Flannery et al., 2013; Kingsley et al., 2007) which share or have similar biochemical and genetic

properties to NoV (Jiang et al., 1993; Kniel, 2014; Wobus et al., 2006). These surrogates are amongst

the most common surrogates used in inactivation studies of NoV in different environments, such as

water, seafood and produce (Bae & Schwab, 2008; Belliot et al., 2008; Bozkurt et al., 2014b; Cannon

et al., 2006; Dawson et al., 2005). Since the proposed viral surrogates can be grown in a cell system

or small animals (Baert et al., 2008; Wobus et al., 2006), they can be used in routine clinical assays

(Kniel, 2014). However, the presence of less structural variations in surrogates compared to the NoV

necessitates the use of multiple surrogates in one study (Kniel, 2014).

Bacteriophages are a group of viruses that infect bacterial cells and share common physical,

biological and chemical characteristics with some mammalian viral pathogens. When viable host is

absent in an environment, bacteriophage cannot replicate themselves. Moreover, their host

specificity is limited to bacteria which means they can only infect bacteria and not mammalian cells,

so they do not pose a risk for humans. Also, they are cheap and generally easy to maintain in the

laboratory (Tufenkji & Emelko, 2011). Therefore, bacteriophage, such as MS2, has been used as NoV

surrogates in studies of enteric viruses.

MS2 is a ssRNA bacteriophage with capsid and known as one of the simplest viruses (Tufenkji &

Emelko, 2011). Compared to other types of phage, MS2 is the most robust model virus to be used in

a viral aerosol study and produced similar results when detected using qPCR and plaque assay

(Turgeon et al., 2014). These properties support the use of MS2 in inactivation and removal studies

of NoV in different types of food including water (Bae & Schwab, 2008; Hornstra et al., 2011), fresh

produce (Dawson et al., 2005), pork (Brandsma et al., 2012) and shellfish (Love et al., 2010).

24

1.4.2. Chlorination

For many years, chlorination, also known as “chlorine-containing disinfectants” treatment (FAO &

WHO, 2009), has been known as an effective treatment to reduce the number of pathogenic

bacteria and viruses in contaminated food. Sodium hypochlorite (NaClO2) as an oxidizing agent is

widely used as a disinfectant in food processing plants because it is cheap and easily applied

(Fonseca, 2006). Moreover, another less harmful chlorine-containing compound such as chlorine

dioxide (ClO2) treatment can be an alternative as it has been legally approved in the US for use as an

anti-microbial agent in food processing (Gómez-López et al., 2009).

Chlorine is a strong oxidizing compound which is able to destroy viral RNA (O'Brien & Newman,

1979) and bacterial cell membranes (Venkobachar et al., 1977). At an appropriate level, this

compound can be directly added into water for drinking (Kitajima et al., 2010) and washing raw food

products such as vegetable (Singh et al., 2002), fruit (Chen & Zhu, 2011) and poultry carcasses (Nagel

et al., 2013; Sarjit & Dykes, 2015) to reduce the level of pathogenic viruses and bacteria.

In NoV inactivation studies, chlorination has successfully reduced the number of the virus (Kim et al.,

2012; Kingsley et al., 2014; Kitajima et al., 2010). These studies reported that chlorination of 0.5

(free chlorine), 189 and 5,000 (total chlorine) ppm were able to reduce NoV by 3.64, 4.14 and 5.26

log10 respectively. In contrast with those studies, a study by Duizer et al. (2004) suggested that 300

ppm total chlorine was ineffective to reduce the number of NoV in the suspension. Factors that may

contribute to the chlorination efficacy are pH, temperature and the presence of organic matter

during inactivation (Hirneisen et al., 2010; Kingsley et al., 2014; Morino et al., 2009; Tung et al.,

2013). As the RT-PCR assay, which may not be able to distinguish between infectious and non-

infectious virus was been used in this study, the different efficacy of chlorination to reduce NoV

might be result of overestimation of infectious NoV.

25

1.4.3. High temperature treatment

The use of high temperature treatment (also known as ’heat treatment’) to inactivate

microorganisms is widely used food preservation technique. In the food industry, there are four

types of heat treatment: pasteurisation, sterilisation, canning and blanching (Teixeira, 2015). In the

meat and fish industries, sterilisation and canning are the most popular treatments. The study of

high temperature treatment in shellfish industries has been done since 30 years ago by Millard et al.

(1987). That study evaluated double boiling or cooking at 85-100°C to inactivate HAV and poliovirus

(PV) during shellfish processing. Using a radioimmunofocus assay, this method was successfully

confirmed to inactivate both viruses. Another study by Hewitt and Greening (2006) also confirmed

the efficacy of heating at 90°C for 90 s to inactivate viral particles in mussel.

More recent studies showed that high temperature treatments ranging from 50-80°C for 0.21-20

min exposure were able to reduce NoV and other NoV surrogates in shellfish (Araud et al., 2016;

Bozkurt et al., 2014b; Croci et al., 2012). Other studies also showed the ability of high temperature

to reduce NoV and its surrogates in different matrixes, such as berries (Butot et al., 2009), water and

milk (Hewitt & Greening, 2006) and PBS (Li et al., 2012; Topping et al., 2009; Wang & Tian, 2014).

However, the efficacy of heat treatment to reduce the NoV depends on the temperature, time

exposure, type of matrix and the initial titers of virus used in the experiments (Arthur & Gibson,

2015). Also NoV shows less susceptible to heat treatment than their surrogates (Knight et al., 2016),

therefore the use of the most heat-resistant surrogate is considered (Arthur & Gibson, 2015).

1.4.4. Mathematical modelling on virus inactivation

In the microbial inactivation, changes in the environment due to high temperature or mild

inactivation treatment such as chlorination, may lead to a log-linear reduction of cell numbers or a

shouldering and tailing expressions (Tamplin, 2005). In the linear phase the decimal reduction time

(D value) is defined as the reduction rate or the time needed to inactivate 90% of the initial

26

population, while, the z value is defined as the changes in temperature needed to produce 90%

change in the reduction rate (D value) (Barer, 2012; Tamplin, 2005).

Predictive modelling in food microbiology is used to describe the growth, survival, inactivation as

well as the metabolic activities of the microorganisms (Buchanan & Whiting, 1997). It can be

categorised based on different approaches, such as the microbial responses toward certain

treatment (growth, survival, and inactivation model), mechanistic or empirical model, and the three-

tier classification (primary, secondary, and tertiary model). A mechanistic model relies on an a priori

knowledge of different factors that influence the behaviour of microorganisms, while an empirical

model uses experimental data from different sets of conditions (Buchanan & Whiting, 1997; Caffi et

al., 2007). Furthermore, Buchanan (1993) defined the three-tier classification of model as follows,

i.e. a primary model that mathematically describes the microbial responses (growth or survival)

towards certain conditions as a factor of time, a secondary model that further describes the effect of

environmental factors on the microbial growth and survival; and a tertiary model that combines

primary and secondary models into a computer program or software.

The empirical model has been widely used in the modelling of microbial inactivation, including viral

inactivation. The first-order kinetic model is a simple linear model assuming that the levels of

cells/virus survival during treatment decrease exponentially over time of exposure. A survival curve

is obtained by plotting the logarithmic number of survival cells/viruses against the lethal dose

received and it is independent to the size of the original population (Barer, 2012). The first-order

kinetic model has been used in studies to predict the effect of thermal processing (Buckow et al.,

2008; Deboosere et al., 2004a; Isbarn et al., 2007; Pecson et al., 2009) and other mild treatments

including HPP (Isbarn et al., 2007) and chlorination (Thurston-Enriquez et al., 2003, 2005) on viral

inactivation. The first-order kinetic model can be described in Equation 1-1 and 1-2 (Erkmen &

Bozoglu, 2016; IFT, 2000; Moats, 1971), and the D value can be calculated from the slope value of

linear regression (Equation 1-3), as below :

27

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= −𝑘𝑘𝑘𝑘 Equation 1-1.

ln � 𝑑𝑑𝑑𝑑0� = −𝑘𝑘𝑘𝑘 Equation 1-2.

D = −1𝑠𝑠 Equation 1-3.

where:


= the rate of viral death

N = the number of virus (PFU/ml) at time (t)

N0 = initial number of virus (PFU/ml)

t = time (h or min)

k = inactivation rate constant

D = 1 log10 reduction (h or min) at time (t)

s = slope of linear regression

This model assumes that each bacterial cell/virus has equal resistance towards the treatments, thus

the death from inactivation occurs to each bacterial cell (Erkmen & Bozoglu, 2016; Moats, 1971).

However, the first-order kinetic theory does not take into account the initial lag in the death rate

(Moats, 1971), while some viral inactivation studies showed that the viral survival curves have

shouldering and tailing phenomena on the beginning and end of the curve (Araud et al., 2016; Chen

et al., 2005; Sigstam et al., 2014). The shouldering and/or tailing phenomena usually occur when

using a high concentration of initial cells and/or at mild heat or lower temperature treatment

(Geeraerd et al., 2000; Tamplin, 2005), or when a subpopulation of virus is resistant to the

disinfectant (Sigstam et al., 2014) or have a low probability of lethal hit by a water molecule during

thermal inactivation (Casolari, 1998). Therefore, the viral inactivation curves often do not follow the

linear model assumptions. In this case, the non-linear models such as Weibull and biphasic models

are used (Araud et al., 2016; Bozkurt et al., 2014b; Sigstam et al., 2014).

The Weibull model, in particular, has successfully estimated virus survival from different treatments.

For example, inactivation of HAV in buffered cell culture treated with HPP (Grove et al., 2009), MNV-

28

1 treated with HPP (Kingsley et al., 2007), HAV in heat-treated oyster (Lee et al., 2015), HAV in

heated blue mussel homogenate (Bozkurt et al., 2014b), HAV, TV, MNV-1 and RV in heated oyster

tissue (Araud et al., 2016), and FCV in pressurised and heated culture media (Chen et al., 2005). In

some of those studies, the Weibull was compared with the linear (first order kinetic) model and was

observed to perform better. The Weibull model is described by the following equation:

𝑙𝑙𝑙𝑙𝑙𝑙 𝑑𝑑𝑑𝑑0

= −𝑏𝑏𝑘𝑘𝑛𝑛 Equation1-4.

where:

N = the number of virus (PFU/ml) at time (t)

N0 = initial number of virus (PFU/ml)

b = the slope factor

t = time (h or min)

n = the scale factor

This model assumes that viral inactivation occurs as probabilities and that the inactivation curve is

the cumulative form of distribution of lethal events (Erkmen & Bozoglu, 2016; Kingsley et al., 2006).

When applying the Weibull model, the D value is usually determined from the linear portion of the

curve (Chen et al., 2005).

Another non-linear model that commonly used to describe the inactivation model of bacteria or

virus is biphasic model (Cerf, 1977; de Roda Husman et al., 2009). This model is based an assumption

that two subpopulation of cells/virus having different levels of resistance to treatments are present

in the bacterial/virus population during inactivation (Cerf, 1977; Humpheson et al., 1998). Therefore,

this model produces two linear curves representing the survival of each subpopulation over the time

exposure thus generate two D value, i.e., D initial and D tailing. The D values of this model can be

generated from both linear regression equation. The Biphasic model (derived from Cerf (1977)) is

described by following equation (Geeraerd et al., 2005):

29

𝑙𝑙𝑙𝑙𝑙𝑙10(𝑘𝑘) = 𝑙𝑙𝑙𝑙𝑙𝑙10(𝑘𝑘0) + 𝑙𝑙𝑙𝑙𝑙𝑙10�𝑓𝑓. 𝑒𝑒−𝑘𝑘𝑚𝑚𝑚𝑚𝑚𝑚1.𝑑𝑑 + (1 − 𝑓𝑓). 𝑒𝑒−𝑘𝑘𝑚𝑚𝑚𝑚𝑚𝑚2.𝑑𝑑� Equation 1-5.

where:

N = the number of virus (PFU/ml or copies/ml) at time (t)

N0 = initial number of virus (PFU/ml or copies/ml)

t = time (h or min)

f = fraction of the initial population in major subpopulation

kmax1 and kmax2 = specific inactivation rate of two population (phase 1 and phase 2, respectively)

1.5. Risk assessment of human NoV in shellfish

Studies on method development and inactivation treatments of NoV have been widely investigated,

however risk assessment of the virus in food and shellfish which comprehensively incorporates these

studies are still limited. For instance, the available risk assessment studies used RT-qPCR method

without pre-treatment which is unable to distinguish between infectious and non-infectious NoV

(Bouwknegt et al., 2015; Masago et al., 2006; Mok et al., 2014; Stals et al., 2015; Suffredini et al.,

2014). Hence, the level of exposure and prevalence data produced from these studies may not

represent the real risk exposure of NoV. Therefore, combining the modified detection methods

which can differentiate infectious and non-infectious virus together with inactivation treatments will

enhance the risk prediction in a risk assessment study.

In addition, published risk assessment studies of NoV have been conducted for developed countries

such as European countries, Japan, and Australia (Bouwknegt et al., 2015; Masago et al., 2006; Stals

et al., 2011; Suffredini et al., 2014), which in general have different shellfish eating behaviour

compared to people in Indonesia.

1.6. Thesis objectives

As the consumption of shellfish continues to increase in Indonesia, the development of an accurate

risk assessment, using a reliable quantification method of NoV and based on the specific eating

30

behaviour of shellfish in Indonesia, is needed to estimate the exposure and risk of NoV in shellfish in

Indonesia.

The overall objective of this research is to estimate the risk of NoV which might contaminate

shellfish in Indonesian fish markets. By applying RT-qPCR with enzyme pre-treatment in NoV

inactivation studies and in NoV prevalence study, a more comprehensive risk assessment will be

developed. There are four aims which contribute to this thesis:

1. To evaluate the application of RT-qPCR pre-treated with enzymes for NoV inactivation

studies.

2. To compare the inactivation kinetic of NoV and its surrogate (MS2 bacteriophage)

treated by heating and chlorine dioxide.

3. To determine the efficacy of high temperature treatment on NoV and MS2 reduction in

buffer and in bioaccumulated-shellfish (inside the tissue).

4. To determine the efficacy of chlorine dioxide as disinfectant to reduce NoV and MS2 in

buffer and in artificially-contaminated shellfish (in the surface)

5. To determine the NoV prevalence in raw shellfish from Indonesian fish markets.

The risk assessment will provide scientific-based recommendations for the Indonesian government

and the related stakeholders. The recommendations can be applied to improve the quality and

safety of shellfish industries as well as provide consumer protection from foodborne outbreaks

related to NoV.

31

Chapter 2. Improving molecular quantification of infectious MS2

bacteriophage: A norovirus surrogate for inactivation studies

2.1. Introduction

NoV is considered to be one of the major causes of foodborne disease globally causing almost 20%

of all cases of acute gastroenteritis worldwide (Ahmed et al., 2014; Karst et al., 2015), or an

estimated 120 million diarrhoeal cases and 5,000 deaths globally in 2010 (Havelaar et al., 2015),

mostly in developing nations. In USA, NoV is estimated to cause 5.46 million foodborne diseases

each year (Scallan et al., 2011). NoV transmission to humans is predominantly by person to person,

followed by food and environmental transmission (Glass et al., 2009; Verhoef et al., 2015). Shellfish,

soft berries and leafy salads are food types that commonly associated with NoV contamination in

food (FAO & WHO, 2008).

A major limitation to study NoV is the difficulty to quantify the viral particles using the previously

developed cell culture system (Cannon et al., 2006; Ettayebi et al., 2016; Patel et al., 2008).

Consequently, RT-qPCR has become a standard diagnostic tool or reference method for NoV

detection and quantification (Glass et al., 2009; ISO, 2013; ISO, 2017; Ushijima et al., 2014).

However, the RT-qPCR assays that are available for detection of total nucleic acid, cannot distinguish

between infectious and non-infectious NoV (Knight et al., 2012): the ribonucleic acid (RNA) from

non-infectious virus remains detectable but undistinguishable by PCR assay even though the virus

has lost its infectivity (Richards, 1999). Therefore, NoV quantification by RT-qPCR assay could over-

estimate the abundance of NoV and hence the risk of illness to humans from NoV in contaminated

food, water or environmental samples.

As previously described in section 1.3 and 1.3.2, many studies have investigated the application of

pre-treatment step to improve the quantification of infectious viral particles and the utilization of

viral surrogates to evaluate its efficacy. For example, RNase is reported to be effective as a pre-

32

treatment in RT-qPCR to quantify only infectious viral particles of NoV (Richards et al., 2012) and its

surrogates such as MNV (Ronnqvist et al., 2013), FCV and HAV (Nuanualsuwan & Cliver, 2002).

However, the efficacy of the RNase pre-treatment appears to depend on the type of virus

inactivation process, especially under harsh inactivation conditions (Pecson et al., 2009; Topping et

al., 2009). From those studies, RNase pre-treatment significantly reduced the amplification of RNA

from non-infectious viral particles by heat treatment. Without further inactivation of RNase

following the pre-treatment, however, RNase may remain in the sample during extraction resulting

in the degradation of RNA from infectious viral particles. This may contribute to under-estimation of

viral abundance by PCR assay.

The strategies to overcome this problem are to eliminate and to inactivate residual RNase activity,

for example, by using of guanidinium thiocyanate and 2-mercaptoethanol during nucleic acid

extraction (Chomczynski & Sacchi, 2006); or adding an RNase inhibitor (RNasin) (Nuanualsuwan &

Cliver, 2002; Yang & Griffiths, 2014); or heating the samples (Johnson, 1996) prior to nucleic acid

extraction. However, heating the samples prior to RNA extraction is not common practice as it may

affect the RNA integrity (Brisco & Morley, 2012) and that results in inaccurate quantification of the

PCR assay. As an alternative to RNase as pre-treatment, the use of different enzymes such as

restriction enzymes is being considered, mainly because the application of these enzymes is cheaper

than RNase+RNasin and safer than the application of 2-mercaptoethanol during nucleic acid

extraction. Molloy and Symons (1980) and Murray et al. (2010) showed that some restriction

enzymes such as HaeIII and TaqI were able to cleave DNA and RNA strands. Hence these enzymes

have potential to be used to disrupt free genomic RNA from inactivated viral particles.

Some authors have proposed the use of cultivable NoV surrogates such as FCV and TV (Cromeans et

al., 2014; Farkas et al., 2010), MNV (Cromeans et al., 2014; Kingsley et al., 2007), FRNA

bacteriophages (Flannery et al., 2013; Hartard et al., 2016) and MS2 bacteriophage (MS2) (Hornstra

et al., 2011) to explore NoV inactivation kinetics. MS2, belongs to genus Levivirus of family

33

Leviviridae, and is a non-harmful cultivable virus which has a similar structure to NoV and has been

frequently used as a NoV surrogate (Brié et al., 2016; Hornstra et al., 2011; Sherchan et al., 2014;

Turgeon et al., 2014). Therefore, the use of MS2 as a NoV surrogate together with the application of

enzymatic pre-treatments, such as RNases and TaqI, could be a promising approach for

quantification methods and for inactivation studies of NoV.

In this study, we examined the performance of an RT-qPCR method with RNase and TaqI pre-

treatments to quantify MS2 bacteriophage as a NoV surrogate and to demonstrate the use of these

methods for the quantification of the NoV surrogate after high temperature and chlorine dioxide

(ClO2) treatments.

2.2. Materials and methods

2.2.1. MS2 bacteriophage stock production

MS2 bacteriophage (MS2) was cultivated as previously described by Bae and Schwab (2008) with the

following modification. MS2 (ATCC® 15597-B1™) purchased from In Vitro Technologies (Australia)

was inoculated into host E. coli strain K12 (culture collection of Tasmanian Institute of Agriculture) at

a ratio of approximately 107 PFU of MS2 per 1010 CFU of E. coli cells in 100 ml of Luria-Bertani (LB)

broth (Oxoid, UK; CM0996) containing 10 mM added calcium chloride (CaCl2) (Sigma Aldrich, USA)

and 0.1% glycine (Sigma Aldrich, USA). The mixture was incubated at 37°C with continuous shaking

for 8 to 12 h until bacterial lysis occurred. Ten ml of chloroform (Sigma Aldrich, USA) was then added

to the suspension and incubated for a further 10 min at 37°C. The culture was then centrifuged at

5,000 x g for 10 min to remove E. coli cells and cell debris, and the virus-containing supernatant was

recovered as MS2 stock. The MS2 stock was serially filtered through 0.45 and 0.22 µm pore-size low-

protein-binding membrane filters (Millipore, Germany) and stored at -80°C. The concentration of

MS2 in the stock was determined as described in Section 2.2. The plaque assays and RT-qPCR results

of infectious MS2 stocks at concentrations from 100 to 107 PFU/µl were compared and analysed by

34

linear regression using Microsoft Excel® (Microsoft, USA), to determine the correlation coefficient

(R2 value).

2.2.2. Quantification of MS2

2.2.2.1. Plaque assay

MS2 were quantified using a double layer agar method (EPA, 2001) with modification, using E. coli

strain K12 as the host strain and LB+ as the culture media. In brief, 3 ml aliquots of semi-solid LB+

agar (LB broth containing 0.7% (w/w) agar, 10 mM CaCl2 and 0.1% glycine) were pre-warmed at 45°C

in a shaking water bath. Then, 100 µl of exponential phase E. coli, containing approximately 106-7

CFU/ml, was added as a host. One hundred µl of serially diluted MS2 stock were added to the pre-

warmed semi-solid LB+ agar (~ 45°C) and then poured into pre-warmed (~ 45°C) 90 mm Petri plates

containing solid LB+ agar (LB broth + 1.5 % (w/w) agar + 10 mM CaCl2 + 0.1% glycine). After 18-24 h of

incubation at 37°C, MS2 were quantified by counting the semi-transparent plaques formed on the

LB+ agar plates. This assay only quantified the presence of MS2 between 2 to 200 PFU per plate.

Therefore, the theoretical limit of quantification (LOQ) of this assay is 2 PFU per 100 µl of sample

that is equivalent to 1.30 log10 PFU/ml.

2.2.2.2. RT-qPCR development

a. Plasmid and standard production

For absolute quantification, a plasmid standard was constructed by cloning nucleotides from 1470 to

2000 of MS2 sequences (GenBank accession no. NC_001417) as previously described by Gentilomi

et al. (2008), with the TOPO II Kit (Invitrogen, USA). The fragment produced had 531 bps length.

Plasmid was purified using a plasmid purification kit (MO BIO, Australia) following the

manufacturer’s recommended procedures and quantified using a Nano Drop 8000 (Thermo

Scientific, USA). Plasmid was linearized by PCR using M13 primers provided with the TOPO II Kit

(Invitrogen, USA). The PCR product had a length of approximately 774 bps encompassing 243 bps of

original M13 sites plus 531 bps of inserted MS2 gene. The product was then purified using a PCR

35

purification kit (MO BIO, Australia) and quantified using a Fragment Analyzer™ (Advanced Analytical

Technology Inc., USA). Standard concentrations for each plasmid used were 10,000,000; 1,000,000;

100,000; 10,000; 1,000; 100; 10; and 1 copies per µl. Copy number of the linearized plasmid was

calculated using Equation 2-1.

Number of copies (molecules) = X ng*6.02221 x 1023 molecules/mole(N*660 g/mole)*1 x 109ng/g

Equation 2-1

Where:

X = amount of amplicon (ng)

N = length of dsDNA amplicon

660 g/mole = average mass of 1 bp dsDNA

b. RNA extraction

Genomic RNA was extracted from liquid samples of MS2 by the acid-guanidinium thiocyanate-

phenol-chloroform method of Chomczynski and Sacchi (2006) with modifications. Specifically, two

hundred µl of liquid sample were mixed with 1 ml denaturing solution (containing 4M guanidinium

thiocyanate, 25 mM sodium citrate pH 7.0, 0.5% N-laurosylsarcosine and 0.1M 2-mercaptoethanol)

and gently shaken for 15 sec. Then 0.1 ml of 2M sodium acetate pH 4.0, 1 ml of water-saturated

phenol, 0.2 ml of chloroform/isoamyl alcohol (49:1) were added and the tubes were shaken

vigorously for 10 sec. The samples were incubated at 4°C for 15 min and centrifuged for 20 min at

10,000 x g at 4°C. The aqueous phase was transferred to new microtubes containing 1 ml of cold

isopropanol (approximately -20°C) (Sigma Aldrich, USA) and incubated at -20°C for at least 1 h. The

RNA pellet was precipitated by centrifugation for 20 min at 10,000 x g at 4°C. After discarding the

supernatant, cold 70% ethanol (approximately -20°C) was added to the pellet and centrifuged for 10

min at 10,000 x g at 4°C. The supernatant was removed from the tubes to isolate the RNA pellet.

After air drying the pellet for 10 min at room temperature (15-25°C), the RNA was dissolved in 50 µl

of DEPC-treated Tris-EDTA (TE) buffer pH 7.2.

36

c. Quantification of MS2 with one-step RT-qPCR

RT-qPCR was conducted using PowerSYBR® Green RNA-to-CT™1-Step Kit (Applied Biosystem, USA)

on a Rotor Gene 3000 (Corbett Research, Australia). Primers used in this assay were designed from

MS2 sequences for nucleotides 1733 – 1804 f (GenBank accession no. NC_001417) analysed using

Primer-BLAST NCBI software. The primer sequences were 5’-GCCGGCCATTCAAACATGAG-3’

(forward) and 5’-CGAGAGAAAGATCGCGAGGAA-3’ (reverse).

PCR thermal condition were as follows: initial holding at 48°C for 30 min and 95° for 10 min;

followed by 45 cycles of denaturation at 95°C for 15 sec; annealing at 55°C for 30 sec; elongation at

72°C for 1 min and final extension at 72°C for 7 min (Gentilomi et al., 2008). The length of the PCR

product amplified from this assay was 92bp. To assess the specificity of PCR product, negative

controls using RNA from bacteria and melt curve assays were conducted following the

recommended procedures for the Rotor Gene 3000 (Corbett Research, Australia).

2.2.3. Preliminary experiment

This preliminary experiment was done in triplicate to confirm the efficacy of RT-qPCR without

enzymatic pre-treatment in MS2 inactivation by heating and chlorination. MS2 suspension was heat-

treated at 72° or treated with 0.5 ppm of chlorine dioxide (ClO2) (Zychem, Australia). For high

temperature treatment, MS2 stocks were heated at 72°C using the methods described by

Nuanualsuwan and Cliver (2002) with modification. In brief, MS2 stocks were added to the pre-

heated (72°C) 2 ml microtubes containing 900 µl PBS to a final concentration of 1010 PFU/ml. The

samples were heated at 72°C for 15, 30, and 60 min in a water bath. The ClO2 treatment was

performed in a 25°C water bath. Appropriate volumes of 10 ppm ClO2 were added to 900 µl MS2 in

PBS stocks (1010 PFU/ml) to reach final concentrations of 0.5 ppm ClO2 and incubated for 15, 30 and

60 min. After incubation, 10 µl of 1% (w/v) sodium thiosulfate was added to the samples which were

incubated for another 10 min to neutralise the oxidising effect of ClO2. Samples from both heat and

37

chlorine dioxide treatments were analysed for MS2 both by plaque assay and RT-qPCR performed as

described in Sections 2.2.2.1 and 2.2.2.2, respectively.

2.2.4. Development of pre-treatment for RT-qPCR

Ten ml of MS2 suspension at a final concentration of 107 - 108 PFU/ml was heat–inactivated at 60°C

for 120 min. This treatment was done to obtain two sub-populations of viruses i.e., infectious and

non-infectious viruses so that, RNA from non-infectious viral particles was present in the suspension.

The heated MS2 were pre-treated with RNase, RNase followed by RNasin (RNase+RNasin), or TaqI

enzyme. All enzymatic pre-treatments and no pre-treatment (control) were done in triplicate. The

RNase+RNasin pre-treatment was carried out as described by Yang and Griffiths (2014) but modified

by adding a 4 µl aliquot containing 10 mg/ml of RNase A (Sigma Aldrich, Germany) to 150 µl of virus

extract and incubating at 35°C for 30 min. Then, 10 µl of RNasin (40 units/µl) (Promega, USA) was

added to the sample and incubated for 30 min at 37°C. For the TaqI pre-treatment, 10 µl of TaqI

enzyme (20 units/µl; NEB, USA) was added to 150 µl aliquots of virus extract and incubated at 60°C

for 30 min. Three control treatments were included: unheated MS2 without pre-treatment,

unheated MS2 with RNase+RNasin pre-treatment and heated MS2 without enzyme pre-treatment.

MS2 RNA was extracted and assayed in triplicate as described in Section 2.2. Results were analysed

using Analysis of Variance (ANOVA) and Tukey Test post-hoc analysis by SigmaPlot 12.0 Version

(Systat Software, USA).

2.2.5. Application of pre-treatment RT-qPCR for inactivation studies

High temperature or chlorination treatments were applied to MS2 suspensions in the inactivation

study. The heat treatments were carried out as previously described in section 2.2.3, with

modification of time exposure. MS2 stock was added to the pre-heated 2 ml microtubes containing

900 µl PBS to a final concentration of 108 PFU/ml. The samples were heated at 72°C for 2.5, 5, 10, 20

and 40 min in a water bath. The ClO2 treatment was performed in a 25°C water bath. Appropriate

volumes of 100 ppm ClO2 were added to the 108 PFU/ml MS2 in PBS stocks to reach final

38

concentrations of 1, 2, 4, 8 and 16 ppm ClO2. After 5 min of incubation, 10µl of 1% (w/v) sodium

thiosulfate was added to the samples and incubated for another 10 min to neutralise the oxidising

effect of chlorine dioxide. The infectious MS2 from both inactivation treatments were assayed in

triplicate by plaque assay and the modified RT-qPCR as described in Sections 2.2 and 2.3. Prior to

nucleic acid extraction, all samples (including control, heated and chlorine dioxide treated samples)

were pre-treated using RNase followed by RNasin.

2.3. Results

2.3.1. The correlation between plaque assay and RT-qPCR

The melt curve analysis showed that the RT-qPCR reaction generated a single peak. Moreover, the

genomic RNA from the negative control was not amplified during the PCR reaction. This indicates

that the assay only amplified the specific target gene of MS2 and that no non-specific amplification

was detected (Figure 2-1A).

Figure 2-1. Melt curve analysis of the standard and samples (A); and standard curve MS2 plasmid from RT-qPCR assay generated from Rotor Gene 3000 (B)

To quantify the MS2 bacteriophage by RT-qPCR, a standard curve was generated from the linearized

MS2 plasmid at concentrations from 100 to 107copies/µl. The RT-qPCR was found to be less sensitive

than the plaque assay with a limit of quantification (LOQ) of 4.46 copies/reaction or 4.46 copies/25

y = -3.39x + 30.01R² = 1.00

Eff.= 0.98535

0

5

10

15

20

25

30

35

-1 0 1 2 3 4 5 6 7

Ct V

alue

Concentration (log copies/µL)

(B)(A)

*Ct value: a fractional number of cycles where the PCR kinetic curve reaches a user or program-defined threshold amount of fluorescence (Schefe et al., 2006).

39

µl (≈ 2.25 log10 copies/ml), while the theoretical LOQ of plaque assay is 1.30 log10 PFU/ml. The

calculated PCR efficiency of the assay was 98% with a slope value of -3.39 and a high correlation of

R2=1.00 (Figure 2-1B).

The correlation between the RT-qPCR and plaque assay were evaluated using only infectious MS2

from unheated stock culture. A high correlation (R2=0.9978, P<0.001) with a slope value of 0.9938

and an intercept value of -0.13 was obtained (Figure 2-2). From the regression equation, the result

from RT-qPCR can be extrapolated to PFU/µl of MS2 where 1 log10 copies/µl is equal to 1.14 log10

PFU/µl.

Figure 2-2. The linear correlation between plaque assay and RT-qPCR on the quantification of infectious MS2

2.3.2. Effect of different pre-treatments on the quantification of mixtures of infectious and non-

infectious MS2

In the preliminary study, MS2 was treated with high temperature and ClO2 to obtain a mixture of

both infectious and non-infectious MS2. The result from plaque assays showed that heating at 72°C

for 15 to 60 min reduced the level of infectious MS2 by 4-9 log10 PFU/ml (Figure 2-3A), while ClO2 at

a concentration of 0.5 ppm from 15 to 60 min had no significant (P>0.05) effect on MS2 reduction

(Figure 2-3B). In comparison to the plaque assay, the result of RT-qPCR without pre-treatment prior

y = 0.9938x - 0.13R² = 0.9978P<0.0001

0

1

2

3

4

5

6

7

8

0 2 4 6 8

RT-q

PCR

(log 1

0co

pies

/ul)

Plaque Assay(log10 PFU/µl)

40

to nucleic acid extraction showed over-quantification of the infectious MS2 after heating at 72°C for

15-60 min. The RT-qPCR result was approximately 1-6 log10 PFU/ml higher than the plaque assay

after the heat-treatment (Figure 2-3A), while after the ClO2 treatment the RT-qPCR assay showed a

similar result to plaque assay (Figure 2-3B).

Figure 2-3. Comparison of RT-qPCR with no pre-treatment (■) and the plaque assay (▧) on the quantification of infectious MS2 after heat treatment at 72°C (A) and chlorination with 0.5 ppm of

ClO2 (B) with LOQ of RT-qPCR (―) and plaque assay (- -).

0

2

4

6

8

10

NoTreatment

72°C 15 min 72°C 30 min 72°C 60 min0

2

4

6

8

10

RT-q

PCR

Assa

ylo

g co

pies

/ml

Treatments

Plaq

ue A

ssay

log

PFU

/ml

(A)

0

2

4

6

8

10

0

2

4

6

8

10

15 min 30 min 60 min

No Treatment 0.5 ppm ClO2

Plaq

ue A

ssay

log

PFU

/ml

RT-q

PCR

log

copi

es/m

l

Treatments

(B)

41

To try to prevent the over-quantification of infectious MS2 by the RT-qPCR assay due to the

presence of genome fragments from non-infectious viruses, enzymatic pre-treatment with RNase,

RNase+RNasin or TaqI was applied prior to RNA extraction. MS2 that had been pre-treated with

RNase, RNase+RNasin or TaqI were analysed using both RT-qPCR and plaque assays. The result of RT-

qPCR pre-treated with RNase+RNasin produced no significant difference (P>0.05) compared to

plaque assays for the quantification of infectious MS2 the heat treatment (Figure 2-4). In contrast,

the RT-qPCR pre-treated either with RNase alone or TaqI produced a significantly different (P<0.001)

result compared to the plaque assay in the quantification of infectious MS2the heat treatment.

Figure 2-4. Quantification of heat-inactivated MS2 with and without enzyme (RNase+RNasin, RNase or TaqI) pre-treatment analysed by RT-qPCR(■) and plaque assay (▧) with LOQ of RT-qPCR (―) and

plaque assay (- -).

Even though the RNase pre-treatment was able to reduce the over-quantification of RT-qPCR, it

under-estimated the number of infectious MS2 by 1.5 log10 PFU/ml compared to the plaque assay

result. Moreover, TaqI pre-treatment slightly reduced the over-quantification of infectious MS2 by

0

2

4

6

8

10

0

2

4

6

8

10

No Pre-Treatment

RNAse +RNAsin Taq I No Pre-Treatment

RNAse +RNAsin RNAse Taq I

Unheated Heated 60°C for 120 min

Plaq

ue A

ssay

log

PFU

/ml

RT-q

PCR

log

copi

es/m

l

Treatments

42

RT-qPCR assay but it still over-estimated the infectious viral particles by 3 log10 PFU/ml. Therefore,

RNase alone and TaqI pre-treatment were not applied in the subsequent inactivation studies.

To evaluate whether the enzymatic pre-treatment affects MS2 propagation, the plaque assay results

of unheated MS2 with RNase+RNasin and TaqI pre-treatment were compared to unheated MS2

without enzymatic pre-treatment (as a control). The plaque assay result showed no significant

difference (P>0.05) between the RNase+RNasin pre-treatment and the control (Figure 2-4). In

contrast, the plaque assay result of MS2 pre-treated with TaqI showed a significant difference

(P<0.001) to the control. The TaqI pre-treatment slightly reduced the number of infectious MS2 by

0.92 log10 PFU/ml.

2.3.3. The application of RT-qPCR with pre-treatment in inactivation study

In the inactivation study, MS2 was treated with heat or chlorination. Since, from the initial study,

exposure to ClO2 at 0.5 ppm did not inactivate MS2, higher concentrations of ClO2 were used in the

subsequent inactivation study. Heat treatment at 72°C (Figure 2-5) and chlorination with 1 – 16 ppm

ClO2 for 5 min (Figure 2-6) were able to inactivate MS2. The result from plaque assay and RT-qPCR

with pre-treatment showed that heating at 72°C for 40 min reduced the number of MS2 up to 5.57

log10 PFU/ml and 4.81 log10 copies/ml, respectively. Furthermore, the chlorine dioxide treatment for

5 min up to 16 ppm showed the reduction of up to 3.46 log10 PFU/ml and 3.46 log10 copies/ml,

respectively. However, the result of RT-qPCR without pre-treatment showed that both heating at

72°C for up to 40 min and chlorine dioxide treatment for 5 min up to 16 ppm resulted in no MS2

reduction.

43

Figure 2-5. MS2 inactivation by heat treatment at 72°C over 40 min as analysed by RT-qPCR without (☐) or with RNase+RNasin pre-treatment () compared to the plaque assay () with LOQ of RT-

qPCR (―) and plaque assay (- -).

Figure 2-6. MS2 inactivation by exposure to different concentration of chlorine dioxide for 5 min at 25°C, analysed by RT-qPCR without (□) or with RNase+RNasin treatment (■) and plaque assay ().

0

2

4

6

8

0

2

4

6

8

0 2 4 6 8 10 12 14 16

Plaq

ue A

ssay

log

PFU

/ml

RT-q

PCR

log

copi

es/m

l

Chlorine Dioxide (ppm) for 5 min at 25°C

(B)

44

2.4. Discussion

Although human NoV can now be cultured in vivo using stem cell-derived human enteroids (Ettayebi

et al., 2016) and can be used to qualitatively evaluate the efficacy of disinfectants for NoV

inactivation (Costantini et al., 2018), however the development of culture-based assay as a simple,

cheap and robust NoV quantification assay remains challenging (Jones et al., 2015). As a solution,

the molecular-based methods, such as RT-qPCR have been widely developed and proposed as the

detection and quantification assay of NoV (Jones et al., 2015; Kirby & Iturriza-Gómara, 2012;

Lowther et al., 2019; Vinjé, 2015). However, the inability of RT-qPCR to distinguish between

infectious and non-infectious viral particles is the major limitation of this assay. RT-qPCR without

sample pre-treatment may detect and quantify the total nucleic acid from both infectious and non-

infectious viral particles, but only infectious NoV particles are able to infect humans and associated

with a risk of human illness. Not knowing the real number of infectious viruses in a mixture of

infectious and non-infectious virus may lead to overestimation of NoV and lead to inappropriate

decisions regarding the risk management of human NoV.

Accordingly, a cultivable NoV surrogate such as MS2 bacteriophage can be used to evaluate the

efficacy of RT-qPCR to quantify infectious viral particles by comparing the calculated number of

copies of viral particles to the plaque assay in which only infectious viral particles are being

quantified. In our studies, the efficacy of RT-qPCR (without pre-treatment) to quantify infectious

viral particles from non-inactivated MS2 stock was evaluated by comparing the RT-qPCR to plaque

assay results. The RT-qPCR gave comparable results and high correlation (R2=0.9994 (P<0.001) with

a slope value of 0.9938 and intercept value of 0.12997) to the plaque assay for the quantification of

infectious viral particles (Figure 2-2). The LOQ of RT-qPCR method used in this study was 4.46

copies/reaction or 4.46 copies/25 µl. This result was comparable to RT-qPCR assay from Rolfe et al.

(2007); Dreier et al. (2005); and O'Connell et al. (2006) where the LOQ were 2 copies/25 µl, 44.9, and

200 copies/20 µl, respectively.

45

However, the RT-qPCR failed to quantify the number of infectious MS2 particles surviving high

temperature treatment for different durations when compared with the plaque assay results. The

numbers of MS2 genomes were constant for all treatments when quantified by RT-qPCR but

declined when enumerated by plaque assay (Figure 2-3). This indicates that the quantification of

infectious viral particles after heat treatment was over-quantified by RT-qPCR without pre-treatment

compared to the plaque assay. In agreement with our result, other studies also reported no

correlation between the numbers of genomic copies detected with RT-qPCR (without pre-treatment

prior to RNA extraction) and the number of infectious viral particles detected by plaque assay after

an inactivation treatment, such as heat (>72°C), chlorination or other type of disinfectant (Baert et

al., 2008; Belliot et al., 2008; Escudero-Abarca et al., 2014; Fraisse et al., 2011), but positive

correlation on the viral quantification were observed between RT-qPCR with pre-treatments (using

RNase or PMA/EMA) and plaque assay results (Escudero-Abarca et al., 2014; Leifels et al., 2015;

Parshionikar et al., 2010). However, the efficacy of pre-treatment prior to RNA extraction may vary

depends on type of virus, matrix types, inactivation treatments and RNA extraction procedure. For

example, pre-treatment using PMA was effective to measure infectious poliovirus surviving from

heat treatment, but less effective for NoV (Parshionikar et al., 2010). None of these studies used

MS2 as a NoV surrogate, but instead used MNV, FCV, PV or HAV. Therefore, the present results

together with those observed in other studies confirm that RT-qPCR without pre-treatment prior to

nucleic acid extraction is insufficient to estimate the levels of infectious viral particles, especially

when applied to particular inactivation treatments.

In our study, enzymatic pre-treatment prior to nucleic acid extraction was used to eliminate free

genomic RNA from the non-infectious MS2 viral particles. The RT-qPCR and plaque assay results of

infectious MS2 in the inactivation experiments showed that the over-quantification of infectious

MS2 from heat treatment can be reduced with the application of RNase or RNase+RNasin prior to

RNA extraction (Figure 2-4). This is because RNase degrades the RNA from non-infectious viral

particles that lack capsid protection, so that only RNA from infectious MS2 was quantified by the RT-

46

qPCR. When MS2 is exposed to 72°C for 10 min, the protein capsid is disrupted (Pecson et al., 2009)

and so the RNA genome from the damaged virus becomes accessible to RNase (Brié et al., 2016).

The ability of RNase to degrade viral genome integrity also depends on the inactivation method and

target viruses used in the assay (Knight et al., 2012; Pecson et al., 2009). For example, in their

inactivation study of HAV, Bhattacharya et al. (2004) showed that the use of RNase as a pre-

treatment in RT-PCR was more effective for UV inactivated samples than when it was used with heat

treated samples.

However, RNases may remain active at low temperature and pH, and continue to degrade RNA

released from infectious viral particles during the nucleic acid extraction and preparation for PCR

assay. For instance, during purification of RNase A from bovine pancreas by a classical procedure,

the enzyme remained stable and active under low temperature and pH (Raines, 1998). Therefore,

the application of RNase as pre-treatment without further inactivation of this enzyme prior to RNA

extraction may result in under-estimation of infectious viral particles. As shown in our enzymatic

pre-treatment studies, the application of RNase without further inactivation by RNasin in RT-qPCR

assay under-estimated the number of infectious MS2 the heat treatment compared to

RNase+RNasin pre-treatment (Figure 2-4).

Furthermore, RT-qPCR with RNase+RNasin pre-treatment also showed similar trend to the plaque

assay result. This indicates that RNase+RNasin pre-treatment can be used to reduce the over-

estimation of infectious MS2 after exposure to high temperature (Figure 2-5) or chlorine dioxide

treatment (Figure 2-6). Our results confirm the observation of Nuanualsuwan and Cliver (2002) that

the RNase is able to eliminate the over-estimation of infectious NoV surrogates such as HAV, vaccine

PV 1 and FCV from UV, chlorine and 72°C inactivation.

RNasin is a protein that inhibits RNA by binding with high affinity to, and blocking the active site of

RNase (Kobe & Deisenhofer, 1996). The addition of RNasin therefore helps to prevent RNA

degradation by residual RNase (Nuanualsuwan & Cliver, 2002; Yang & Griffiths, 2014), which might

47

result in under-estimation of the infectious MS2. In our study, RNasin was used along with the

application of RNase as pre-treatment prior to nucleic acid extraction. Results from the RT-qPCR

showed that the significant difference (P<0.001) between RNase with and without subsequent

RNasin treatment was observed (Figure 2-4). Moreover, no significant difference (P<0.001) was

observed in plaque assay results between RNase+RNasin pre-treatment and no pre-treatment (as a

control). These indicated that the enzymatic pre-treatment of RNase followed by RNasin might not

injure the infectious MS2 or might not interfere the propagation of infectious MS2 into the host cell.

Thus, RNase+RNasin is potentially to be applied as a pre-treatment prior to nucleic acid extraction

for the RT-qPCR assay to enumerate the infectious virus from the inactivation.

As an alternative to RNase+RNasin, we evaluated the use of restriction endonucleases such as TaqI

as a pre-treatment. This class of enzyme is cheaper than RNase and simpler to use because they

provide a one-step pre-treatment rather than the two-step RNase then RNasin protocol. To the best

of our knowledge, however, the use of TaqI as a pre-treatment has not been widely reported. Our

RT-qPCR results showed that the use of TaqI reduced the over-estimation of MS2 by 1 log10

copies/ml; however, in comparison with the plaque assay, it still overestimated the amount of

infectious virus by approximately 3 log10 PFU/ml. In addition, the results of the plaque assay after

this pre-treatment indicates that either TaqI may affect the lysogenic cycle of MS2 into the host cell

(E. coli strain K12) or incubation at 60°C for 30 min may inactivate MS2 as the number of MS2 were

approximately 1 log10 PFU/ml lower than in the control (without pre-treatment) (Figure 2-4). As a

result, the use of TaqI as a pre-treatment may not be as useful as RNase, and further optimisation is

needed before applying this enzyme in future studies.

High temperature has been shown to be an effective treatment to reduce the number of infectious

NoV and its surrogates including MS2 either in the foods, shellfish, water or culture medium (Araud

et al., 2016; Bozkurt et al., 2014b; Brié et al., 2016; Buckow et al., 2008; Mormann et al., 2010;

Tuladhar et al., 2012). It works by changing the structure of the capsid protein of the viruses (Baert

et al., 2008; Nuanualsuwan & Cliver, 2003), and potentially jeopardising RNA integrity, which may

48

affect their ability to initiate the infectious process (Cliver, 2009). We also observed that heat

inactivation at 72°C for 20 to 40 min was effective and reduced the number of MS2 by 5-5.5 log10

PFU/ml (Figure 2-5). Moreover, heating at 76.6°C for 2 min has been suggested as the minimum

temperature to eliminate 4-5 log10 copies/reaction of NoV by heat inactivation modelling (Beller et

al., 1997; Topping et al., 2009). Therefore, the application of heat treatment in food preparation

such as steaming, boiling and cooking might be an effective method to eliminate enteric viruses

including NoV in food.

Oxidative chemical substances such as chlorine and ClO2 are alternative disinfectants to inactivate

enteric virus on food contact surfaces (Feliciano et al., 2012; Kim et al., 2012) and in uncooked food

(Predmore & Li, 2011). Chlorine dioxide causes oxidative damage to the RNA genome and reacts

with the capsid protein thus preventing virus attachment to the host cell (Li et al., 2004). A study

from Hornstra et al. (2011) confirmed that the application of 0.5 ppm ClO2 using a reactor was

sufficient to inactivate MS2 by up to 5 log10 unit after an exposure time of at least 20 min. This

contrasts with our preliminary study using the plaque assay that found that the application of 0.5

ppm ClO2 for 15-60 min did not inactivate MS2. This difference was probably due to the use of a

reactor in the previous study which maintains the concentration of ClO2 constant during the

treatment. When higher concentrations of ClO2, up to 16 ppm for 5 min, were applied in our

inactivation studies, ClO2 inactivated MS2 by up to 3 log10 PFU/ml (Figure 2-6). The different

inactivation efficacy between our preliminary and inactivation studies may be due to the tailing

phenomenon which occurs during chlorine or ClO2 inactivation processes (Hornstra et al., 2011;

Sigstam et al., 2014); therefore the concentration of ClO2 is not linearly correlated with the viral

inactivation.

Our inactivation studies showed that both heat and ClO2 treatment have the potential to be applied

to eliminate and to reduce viral particles that may contaminate food, water or food contact surfaces.

The use of ClO2 might be a good alternative disinfectant to eliminate or to reduce the viruses that

are transmitted to food via the secondary transmission such as contaminated water or infected-

49

person hand during food handling, but might be ineffective to eliminate NoV inside the shellfish

tissue, which originates from the natural contamination. This ClO2 treatments can be done by

dipping, washing or cleaning processes when it is not possible to use heat treatment for uncooked

food products such as raw oysters, fresh fruits and vegetables.

2.5. Conclusions

In this study, the quantification of MS2 bacteriophage (as a NoV surrogate) after exposure to heat or

chlorine dioxide using RT-qPCR without RNase pre-treatment overestimated the number of

infectious viruses, while RT-qPCR with RNase-only pre-treatment underestimated the number of

infectious viruses. Hence, the use of RNasin as a complimentary step after RNase pre-treatment is

required for the RT-qPCR assay to produce a comparable result to a plaque assay in the

quantification of infectious viral particles. The results of the present study, therefore, demonstrate

the potential for using such an approach to more accurately determine the infectious viral particles

of “uncultivable” virus where the viral capsid integrity is the object of inactivation, such as NoV

surviving from inactivation by heat or chlorine dioxide. This pre-treatment might not be suitable to

determine surviving viral particles from inactivation by UV or irradiation where the viral genome

integrity is the object of inactivation.

50

Chapter 3. Thermal inactivation kinetics of Human norovirus and MS2

bacteriophage in buffered media and bioaccumulated Tasmanian Blue

Mussel (Mytilus galloprovincialis)

3.1. Introduction

NoV is one of the most prominent foodborne viruses that cause enteritic disease (Koopmans et al.,

2008) and is frequently related to consumption of virus-contaminated shellfish (Le Guyader et al.,

2010). There are numerous outbreak reports of NoV contamination from shellfish in U.S.A. (Berg et

al., 2000; Kohn et al., 1995), European countries (Le Guyader et al., 2006; Westrell et al., 2010),

Australia (Webby et al., 2007), and Singapore (Ng et al., 2005). Although most of the outbreaks

caused by NoV were associated with the consumption of raw oysters, undercooked shellfish also

contributed to outbreaks (Alfano-Sobsey et al., 2012; Richards, 2006). When cooking is applied,

temperature and holding time play important roles during cooking, and are considered as critical

points in reducing the incidence of NoV-foodborne cases.

In countries where shellfish is consumed as a cooked meal, the application of thermal inactivation by

heating can greatly reduce the risk of gastrointestinal disease, without concerning the change of

organoleptic quality. Thermal inactivation is considered as one of the most effective treatments to

reduce the number of enteric viruses that contaminated food and drinking water (Bertrand et al.,

2012). There is high variability in the efficacy of this treatment, which depends on the matrix types

and sizes, the virus species or strains, detection or quantification methods (Bertrand et al., 2012;

Bozkurt et al., 2015b) and holding time (Arthur & Gibson, 2015). As expected, inactivation rates at

≥50°C are faster than at <50°C (Bertrand et al., 2012), hence, heating at ≥50°C has potential to be

applied in food processing to reduce the risk of NoV infection.

Studies of heat inactivation of enteric viruses have been initiated since 1960’s (Heberling & Cheever,

1960). However, determining the heat inactivation kinetics of viruses such as NoV , SaV, and HEV

51

remains challenging (Bozkurt et al., 2015b; Koopmans & Duizer, 2004; Randazzo et al., 2018) due to

the absence of an effective and robust cell culture-based system as a standard quantification

method (Harrison & DiCaprio, 2018; Oka et al., 2015). Consequently, molecular-based method such

as PCR, and the culturable surrogates that have a similar structure to the targeted viruses, have been

commonly applied in heat inactivation studies (Flannery et al., 2014; Randazzo et al., 2018; Richards,

2012). Since the inactivation kinetics of these surrogates is varied, thus, a study comparing the

inactivation kinetics between the actual virus and a surrogate would be beneficial to reduce

underestimation or overestimation of the inactivation kinetics.

In the last few decades, several mathematical models have been used to describe the viral

inactivation kinetics and to evaluate the efficacy of thermal inactivation in reducing enteric viruses in

food and water (Deboosere et al., 2004b; Deboosere et al., 2010; Kauppinen & Miettinen, 2017;

Romero et al., 2011) or their surrogates (Bozkurt et al., 2013, 2014a; Hewitt et al., 2009). Linear and

non-linear regression models have been applied to describe and to predict the inactivation kinetics

in these studies. First-order kinetic and log-logistic equations were widely used as linear models to

generate D and z values for thermal inactivation of enteric viruses, while Weibull and Biphasic

models were used to describe more complex inactivation kinetics (Araud et al., 2016; Bertrand et al.,

2012; Seo et al., 2012; Tuladhar et al., 2012). Although some of studies have explored both model

types (linear and non-linear) to determine the thermal inactivation kinetics, there are few studies on

heat inactivation kinetics of human NoV and its surrogates which incorporate or compare both

models.

In this study, a pre-treatment RT-qPCR was used as a quantification method for NoV during the heat

inactivation study. Pre-treatment RT-qPCR has been used in some studies to enumerate NoV in the

sample that contains both infectious and non-infectious viruses. The use of substances such as EMA,

PMA/PMAXX, proteinase K and RNase as a pre-treatment in RT-qPCR has been shown to reduce the

overestimation of infectious viral particles (Barbeau et al., 2005; Karim et al., 2015; Nuanualsuwan &

Cliver, 2002; Oristo et al., 2018). Also, MS2 bacteriophage has been proposed as a surrogate for NoV

52

inactivation studies because of its structural similarity with NoV, and because it is easy to handle and

cheap (Tufenkji & Emelko, 2011). To the best of the candidate’s knowledge, there is no study that

has evaluated the heat inactivation kinetics of MS2 and infectious NoV (which was quantified by pre-

treatment RT-qPCR) using both linear and non-linear model approaches.

The purposes of the present study were to evaluate and to compare thermal inactivation kinetics of

NoV and its surrogate (MS2) in buffered media and Tasmanian Blue Mussel (Mytilus

galloprovincialis) matrix utilising different models (i.e. the log linear, Weibull and Biphasic model).

Mussels were artificially contaminated by the bioaccumulation process to mimic the actual condition

of enteric virus’s transmission routes in shellfish. Viruses in buffered media and contaminated-

mussel were treated with different temperatures and holding times.


3.2.1. NoV stock preparation

Eight fresh faecal specimens containing NoV genogrup II genotype 4 (GII.4) were provided by the

Hobart Pathology, Hobart, Tasmania. These samples were previously determined to be NoV-positive

by an immunochromatographic test using Rida®Quick (Biopharm AG, Gemany) (Bruggink et al., 2011;

Bruins et al., 2010; Kirby et al., 2010). All samples were prepared as previously described by Trujillo

et al. (2006) with some modifications, described here. In brief, 1 g of faecal/stool or 1 ml of watery

stool was suspended in 9 ml of PBS (Phosphate Buffered Saline) that was previously prepared in

diethyl pyrocarbonate-treated water, yielding a 10% suspension. The suspension was then added to

5 ml chloroform and vigorously shaken for 30 sec. The virus was then separated from the organic

matter by centrifugation at 10,000 x g for 10 min at 4°C. The upper aqueous phase was transferred

to new, sterile, 50 ml plastic tubes and serially filtered through 0.45 and 0.22 µm pore-size low-

protein-binding membrane filters (Millipore, USA). The virus stock was stored at -80°C for

subsequent studies. RT-qPCR assay with enzyme pre-treatment was performed to determine the

NoV concentration on the virus stock. The specific primers COG2R and QINF2 were used to quantify

53

the NoV GII.4 because of their specificity and sensitivity (International Organization for

Standardization, 2013; Loisy et al., 2005; Miura et al., 2013). Virus stocks with concentration of >109

genomic copies per ml were used for inactivation studies.

3.2.2. MS2 bacteriophage stock production

MS2 bacteriophage (MS2) was produced as previously described in Section 2.2.1. of this thesis. The

concentration of infectious MS2 in the stock was confirmed by a double layer agar method (EPA,

2001). The concentration of infectious MS2 was expected to be between 1011 to 1012 PFU/ml.

3.2.3. Bioaccumulation in mussels

Five kilograms of live Tasmanian Blue Mussel (Mytilus galloprovincialis) were purchased from a

single local supplier in Tasmania and kept at <10°C during transportation. Three individual mussels

per batch (1 batch equal to 1 kg mussel) were randomly picked and analysed by RT-qPCR and plaque

assay to detect the presence of MS2 and NoV as natural contaminants. In the screening step,

naturally contaminated batches of mussels (with MS2 or NoV) and mussels with broken shells were

not used for the bioaccumulation study. None of the mussel batches were naturally contaminated by

NoV and MS2.

Only four kilograms of mussels (50-60 individual mussels/kg/batches) were obtained from the

screening step, and then were acclimated for 24 h in an aquarium (40 x 25 x 50 cm) using 20 l of

sterile artificial seawater with continuous aeration. After the acclimatisation, 100 live mussels were

selected for the bioaccumulation process. The mussels were laid on a monolayer disposal in 10 l of

sterile artificial sea water (containing 2% of sea salt) which was contaminated with NoV and MS2

stock. The final concentration of NoV and MS2 in the aquarium seawater was approximately 107 -108

copies/ml and 108-109 PFU/ml, respectively. To optimize the bioaccumulation process in the DT of

mussels, 10 ml of concentrated phytoplankton (Reef PhytoplanktonTM, Australia) was added to the

seawater. The bioaccumulation process was conducted for 12 and 24 h at 10 ± 4°C, under similar

condition to the acclimatisation step. After bioaccumulation process, all mussels were dipped in 20 l

54

sterile seawater for 5 min to remove contaminated-water from the mussel body. Three individual

mussels were dissected to take out the tissue. The mussel tissues were washed with sterile saline

water (ddH2O+0.9% NaCl), extracted and then analysed using RT-qPCR and/or plaque assay to

quantify the NoV and MS2 concentration. Each mussel tissue was weighed and recorded prior to

sample extraction. The bioaccumulation process is presented in Figure 3-1.

3.2.4. Thermal inactivation in buffered media

The temperature of buffered media (PBS) in 15 ml plastic tubes was equilibrated by heat pre-

treatment for 10 min at 60, 72 and 90°C for thermal treatments, or at 20°C for controls. NoV and

MS2 stocks were added to make final concentrations of approximately 107 copies/ml and 108

PFU/ml, respectively. The suspensions were heated using water bath at 60±1°C for 15, 30, 60, 120

and 240 min; 72±1°C for 2.5, 5, 10, 20 and 40 min; and 90±1°C for 1, 2.5, 5, 10, and 20 min. Each

treatment was done in triplicates. Thermocouple Tecpel 319® (Taiwan) with 4-channel wired probes

were used to confirm the actual temperature in the tubes during treatment. After each incubation

time, samples were taken from water bath and kept in a freezer at -20°C for further analysis.

55

Figure 3-1. Acclimatisation and bioaccumulation process of Tasmanian Blue Mussel (Mytilus galloprovincialis)

Stock

3 mussels analyse RT-qPCR & Plaque assay

4 kg mussel (200-240 pcs) acclimatisation 24 h at 10±4°C

Bucket + 5 kg live mussels

2 kg mussels/ 10 litre artificial sea water

100 ml of NoV and MS stock

Phytoplankton concentrate

10±4°C

Plaque assay

12 & 24 h bioaccumulation process

Sampling for RT-qPCR & Plaque assay

56

3.2.5. Thermal inactivation in mussel matrix

The thermal inactivation treatments were done in triplicate in water baths at 60, 72 and 90°±1C.

Previously, forty five of 30 ml of PBS solution in 50 ml plastic tubes were pre-heated at certain

temperatures (for 30 min) to equilibrate the thermal condition. The PBS solution was used as a

buffer media in this study to avoid a viral aggregation due to changes in the environment before the

heat treatment. Two pieces of bioaccumulated-mussels tissue (approximately 10-14 g) were then

added to each suspension/tube and heated for specified contact times as shown in Table 3-1. After

each incubation period, five grams of mussels were removed from the water bath and transferred to

a freezer at -20°C before subsequent concentration and purification steps.

Table 3-1. Contact times of thermal inactivation at different temperatures.

Treatments Replication Contact Time (Min)

Control (No Heating/±20°C) 3 0

60°C 3 15; 30; 60; 120; and 240

72°C 3 2.5; 5; 10; 20; and 40

90°C 3 1; 2.5; 5; 10; and 20

3.2.6. Enumeration of NoV and MS2

3.2.6.1. Virus concentration

Viruses were isolated and concentrated form the mussel samples following the procedure of Lewis

and Metcalf (1988) and Mullendore et al. (2001), with modifications. In brief, 5 g of whole mussel

tissue were homogenized by shaking at 250 rpm with 1:6 (w/v) 10% tryptose phosphate broth (TPB)

in 0.05 M glycine (pH 9.0) for 30 min at 4°C. Seven ml of supernatant was transferred into new 15 ml

plastic tubes, 5 ml of chloroform was added, and centrifuged at 10,000 x g for 10 min at 4°C. The

upper aqueous phase was transferred into 7 ml of 16% PEG 6000 (Sigma Aldrich, USA) and 0.6 M

NaCl (pH 6.5), and was shaken at 250 rpm for 12 h at 4°C. The resulting suspension was centrifuged

at 10,000 x g for 30 min at 4°C. The PEG-containing supernatant was discarded, and the pellet was

57

suspended in 1 ml PBS, pH 7.5 sonicated for 30 s, shaken for 20 min at 250 rpm. The suspension was

re-purified by adding an equal volume of chloroform, and centrifuged at 10,000 x g for 10 min at

4°C. The upper aqueous phase was then transferred into new 2 ml micro tube and stored at -20°C.

3.2.6.2. Enzymatic pre-treatment prior to RNA extraction

Prior to nucleic acid extraction, heat treated, as well as control samples, were enzymatically pre-

treated as previously described in Section 2.2.4. of this thesis. Subsequently, RNA in samples was

extracted by the acid-guanidinium thiocyanate-phenol-chloroform method of Chomczynski and

Sacchi (2006), with modification, as previously described in Section 2.2.2.2.b. of this thesis.

3.2.6.3. Quantification of infectious NoV by RT-qPCR assay

For NoV GII assay, the RT-qPCR was performed as previously described by Jothikumar et al. (2005)

with modifications using PowerSYBR® Green RNA-to-CT™1-Step Kit (Applied Biosystem, USA) on the

Rotor-Gene Q (Qiagen, Germany). JJV2F and COG2R primers were used as forward and reverse

primers, respectively. In the final mixture, the RT-qPCR reaction contained 5 µl of RNA template, 0.5

µl of each primer (final concentration of 250 nM), 10 µl of 2x PowerSYBR® Green buffer, 0.2 µl RT-

Taq enzyme, and DNase/RNase-free purified-water to make a final volume of 20 µl. The mixture was

then subjected to a one-step assay by using the following amplification conditions: (i) RT for 30 min

at 48°C, (ii) 10 min at 95°C to activate Taq polymerase, and (iii) 45 cycles of 10 s at 94°C, 20 s at 55°C,

and 15 s at 72°C. To develop a standard for enumeration of NoV GII, a plasmid was constructed by

cloning nucleotides from 4830-5285 of GII.4 Lordsdale NoV sequences (GenBank accession no.

X86557) from isolated NoV. The 475 bps plasmid fragment encompassed 97 bps of RT-qPCR product

sequences. The plasmid was purified and serially diluted in free DNase/RNase purified-water. To

assess the specificity of PCR products, negative controls using RNA from E. coli K12 bacteria and MS2

was used, and the melt curve analysis were performed following the procedures from the Rotor-

Gene Q® (Qiagen, Germany).

58

3.2.6.4. Quantification of infectious MS2 by plaque assay

The infectious MS2 was quantified by plaque assay as described in Section 2.2.2.2.a of this thesis.

3.2.7. Modelling of thermal inactivation kinetics

Three different models i.e. log linear (first-order kinetic), Weibull and Biphasic, were compared to

obtain the best fitted survival curve of NoV and MS2. The first-order kinetic model is written as

follows (Geeraerd et al., 2000):

𝑙𝑙𝑙𝑙𝑙𝑙 � 𝑑𝑑𝑑𝑑0� = − 𝑑𝑑

𝐷𝐷 or 𝑙𝑙𝑙𝑙𝑙𝑙10(𝑘𝑘) = 𝑙𝑙𝑙𝑙𝑙𝑙10 (𝑘𝑘0) − 𝑘𝑘𝑚𝑚𝑚𝑚𝑚𝑚.𝑑𝑑

𝑙𝑙𝑛𝑛(10) (Equation 3-1)

In the cases of shoulder and/or tailing phenomenon, the modified log-linear model with shoulder

and/or tailing can be applied to fit the curves. The modified model proposed by Geeraerd et al.

(2000) is written as follows.


= −𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘.𝑘𝑘. � 11+𝐶𝐶𝑐𝑐

� . �1 − 𝑑𝑑𝑟𝑟𝑟𝑟𝑟𝑟𝑑𝑑� (Equation 3-2)

where N is the number of viruses survived after heat treatment and No is the initial population of

viruses. MS2 population was described in PFU/ml, while NoV was quantified in copies/ml. t is the

exposure time (min), kmax is the first order inactivation constant [1/min] and D (decimal reduction

time) is the time required to eliminate 90% of the population (min). Herein, Cc is related to the

physiological state of cells or viruses [-], and Nres is the residual population density (PFU/ml or

copies/ml).

The Weibull model equation proposed by Mafart et al. (2002) is described as follows:

𝑙𝑙𝑙𝑙𝑙𝑙 � 𝑑𝑑𝑑𝑑0� = −�𝑑𝑑

𝛿𝛿�𝑝𝑝

or 𝑛𝑛 = �𝑑𝑑𝛿𝛿�𝑝𝑝

(Equation 3-3)

59

The modified Weibull model which describes concave, convex or linear curves followed by tailing can

be used to fit data with tailing phenomenon. The model was proposed by Albert and Mafart (2005)

and can be written as follows:

l𝑙𝑙𝑙𝑙10(𝑘𝑘) = 𝐿𝐿𝑙𝑙𝑙𝑙10 �(𝑘𝑘0 − 𝑘𝑘𝑟𝑟𝑟𝑟𝑠𝑠). 10�−�𝑡𝑡𝛿𝛿�𝑝𝑝� + 𝑘𝑘𝑟𝑟𝑟𝑟𝑠𝑠� (Equation 3-4)

where δ is the time to first decimal reduction, p is a shape parameter, and n represents the decimal

reduction ratio. The value of δ is not equal to the conventional D value. Therefore, n can be used to

calculate log10 reductions (D), from which 1D can be calculated as n=1, or 2D equal to n=2.

The biphasic model equation (Geeraerd et al., 2005; Schielke et al., 2011) can be generated from

Cerf (1977), as described below:

𝑙𝑙𝑙𝑙𝑙𝑙(𝑘𝑘) = 𝑙𝑙𝑙𝑙𝑙𝑙(𝑘𝑘0) + 𝑙𝑙𝑙𝑙𝑙𝑙�𝑓𝑓. 𝑒𝑒−𝑘𝑘𝑚𝑚𝑚𝑚𝑚𝑚1.𝑑𝑑 + (1 − 𝑓𝑓). 𝑒𝑒−𝑘𝑘𝑚𝑚𝑚𝑚𝑚𝑚2.𝑑𝑑� (Equation 3-5)

where f is the fraction of initial population in a major subpopulation, kmax1 and kmax2 is specific

inactivation rate (1/time unit) at phase 1 (Initial) and 2 (Tailing), respectively.

Curves were fitted to those three models using GInaFiT for Microsoft Excel (Geeraerd et al., 2005).

The 2D, 4D and D Initial values were calculated using Solver® Add-in of Microsoft 365 (Microsoft

Corp).

3.2.8. Statistical analysis

The models were evaluated for the best fit by comparing the Root Mean Square Error (RMSE) and

the coefficient of determination (R2) value for the various models. To measure goodness-of-fit, the

RMSE was used for both linear and non-linear models (Ratkowsky, 2004), while the R2 was only used

for linear models. The RMSE and R2 values were calculated using Microsoft Excel® software. The

RMSE and R2 equation are described below:

60

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = �Σ(𝑝𝑝𝑟𝑟𝑟𝑟𝑑𝑑𝑝𝑝𝑝𝑝𝑑𝑑𝑟𝑟𝑑𝑑−𝑜𝑜𝑜𝑜𝑠𝑠𝑟𝑟𝑟𝑟𝑜𝑜𝑟𝑟𝑑𝑑)2

𝑛𝑛 − 𝑝𝑝 (Equation 3-6)

𝑅𝑅2 = 𝑛𝑛(∑𝑚𝑚𝑥𝑥)−(∑𝑚𝑚)(∑𝑥𝑥)�[𝑛𝑛∑𝑚𝑚2−(∑𝑚𝑚)2][𝑛𝑛∑𝑥𝑥2−(∑𝑥𝑥)2]

(Equation 3-7)

where n is the number of observations and p is the number of parameters to be estimated. The value

of x presents the independent variables (temperature), while y presents the dependent variables

(observed values).

3.3. Results

3.3.1. Bioaccumulation of NoV and MS2 in mussel

The preliminary screening confirmed that there were undetectable levels of NoV or MS2 in batches

of mussel used in the bioaccumulation study. Four batches of mussels (Approximately 50-60

mussels/batch) were acclimated for 24 h. During 12 and 24 h of bioaccumulation process, the mussel

was contaminated by NoV at 6.64 and 6.61 log10 copies/g and MS2 at 7.80 and 7.57 log10 PFU/g,

respectively. There were no significant differences (p>0.05) in viral concentration in mussels that

were subjected to bioaccumulation for 12 or 24 h for either virus as shown in Table 3-2. However,

unopened shells were observed in the mussels (<10%) after 24 h bioaccumulation process which

may indicate dead or inactive mussels. Therefore, only mussels from the 12 h of bioaccumulation

were used for inactivation studies to reduce the variability of initial concentration of NoV or MS2.

Table 3-2. The concentration of NoV and MS2 in seawater and mussel after bioaccumulation process for 12 and 24 h.

Virus Seawater

(24 h) Unit

Mussel Unit

12 h 24 h

NoV 7.67 ± 0.05 log10 copies/ml 6.64 ± 0.17a 6.06 ± 0.63a log10 copies/g

MS2 8.12 ± 0.24 log10 PFU/ml 7.80 ± 0.03b 7.57 ± 0.27b log10 PFU/g

*The same letter in the same row denotes no significant differences (p>0.05)

61

3.3.2. Thermal inactivation of NoV and MS2

To determine the thermal inactivation kinetics of NoV and MS2 in buffered media and mussel

matrix, the virus stock and bioaccumulated-mussels were exposed to heat treatment at 60, 72 and

90±1°C for defined periods. The concentration of infectious NoV and MS2 after heating in both

matrices, expressed as log10 copies/g or copies/ml and log10 PFU/g or PFU/ml, were plotted against

the contact time (min) at each temperature as shown in Figure 3-2 to 3-5. The average initial

concentrations of NoV in buffer and mussels were 6.26 ± 0.16 log10 copies/ml and 6.64 ± 0.17 log10

copies/g, respectively. While the MS2 initial concentrations were 7.89 ± 0.07 log10 PFU/ml in buffer

and 7.80 ± 0.03 log10 PFU/g in mussel matrix.

The average of NoV reduction in buffered media by heating at 60°C for 240 min, 72°C for 40 min and

90°C for 20 min were 2.81, 2.96 and 3.88 log10 reductions respectively, while the inactivation in

mussel matrix were 2.85, 3.08 and 3.58 log10 reductions at the end of treatment. Furthermore, the

inactivation of MS2 at 60, 72 and 90°C in buffered media and mussel matrix resulted in 4.93, 6.73

and 7.09 and 4.64, 5.42 and 6.35 log10 reductions, respectively. Apparently, based on the log

reductions trends after the treatment, MS2 were more susceptible to heat treatment than NoV in

both buffered media and mussel matrix at each heating temperature. For example, the average of

MS2 reduction in buffered media by heating at 72°C for 10 min resulted in 4.74 log10 reductions, two

logs higher than the reductions of NoV from similar treatment, which was only 2.03 log10 reduction.

Moreover, similar treatment in the mussel matrix reduced MS2 by 3.30 log10 reductions in average,

while only 2.35 log10 reductions were observed from NoV.

3.3.3. Model fitting and comparison

Linear and non-linear models (see Section 3.2.7.) were used to describe the inactivation kinetics and

the times required to a log10 reduction (D value) of NoV and MS2 due to thermal inactivation over

the time. The data of infectious NoV and MS2 over time during heat treatments at 60, 72 and 90±1°C

were fitted using log linear, Weibull and Biphasic models. Since the observed survival of NoV and

62

MS2 data showed a tailing and/or shoulder phenomenon (Figure 3-2 to 3-5), the models were

calculated by modified equations that included terms for tailing and/or shoulders (see Section 3.2.8).

RMSE and/or R2 were used to compare linear and non-linear models, and were also used to

determine the best predicted 2D (time to 100-fold reduction) and 4D (time to 10,000 fold reduction)

values. During the model fitting, the unmodified log linear model produced a lower coefficient of

determination (R2) compared to the log linear model with tailing and/or shoulder (data not shown),

therefore only the modified (with tailing and/or shoulder) log linear model was used for model

comparison.

In general, Weibull (without tailing) and Biphasic models presented better predictions of thermal

inactivation kinetics in both matrices for both viruses, as shown on Table 3-3 and 3-4. Some

inactivation curves were better fitted by Weibull-tailing or Log linear-tailing model than Weibull or

Biphasic, especially to predict the infectious viruses in the full duration of the treatment. However,

with the assumption that viruses will not survive from heating for extended periods, Weibull-tailing

and Log linear-tailing models failed to predict the infectious viruses for extended periods (outside of

the full duration of the treatment), because of a constant survival of viruses after certain exposure

time (Figure 3-2 to 3-5). Moreover, based on these observations, Weibull (without tailing) was better

to predict the infectious viruses from heat treatment in the buffered media, while Biphasic

performed better to predict the virus survival in the mussel matrix.

By visually comparing the observed data to the fitted curves of each model, the log linear with tailing

model underestimated and/or overestimated the observed infectious population of NoV and MS2,

especially at initial contact time (t=0) and the end of treatment, while the Weibull or Biphasic

models presented better predictions (Figure 3-2 to 3-5). These subjective evaluations were

consistent with curve fitting analyses (Table 3-3 and 3-4) where the RMSE value of the log linear-

tailing model were always higher than the Weibull or Biphasic, except for NoV heated at 60°C in

mussel matrix.

63

Figure 3-2. Thermal inactivation curves of NoV at 60 (A); 72 (B) and 90°C (C) in buffered media fitted with Log linear-tailing (…), Weibull ( ̶ ̶ ̶) and Biphasic ( —) model.

0

1

2

3

4

5

6

7

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

ml)

Time (min)

(A)

0

1

2

3

4

5

6

7

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

ml)

Time (min)

(B)

0

1

2

3

4

5

6

7

0 50 100 150 200 250

Log 1

0(N

) (lo

g co

pies

/ml)

Time (min)

(C)

*LOQ = 2.40 log10 copies/ml

64

Figure 3-3. Thermal inactivation curves of MS2 at 60 (A); 72 (B) and 90°C (C) in buffered media fitted with Log linear-tailing (…), Weibull ( ̶ ̶ ̶), Weibull-tailing ( ̶ · ̶ ) and Biphasic ( —) model.

0

1

2

3

4

5

6

7

8

9

0 50 100 150 200 250

Log 1

0(N

)(log

PFU

/ml)

Time (min)

(A)

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

PFU

/ml)

Time (min)

(B)

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

PFU

/ml)

Time (min)

(C)

*LOQ = 0.70 log10 PFU/ml (---)

65

Figure 3-4. Thermal inactivation curves of NoV at 60 (A); 72 (B) and 90°C (C) in mussel matrix fitted with Log linear-tailing (…), Weibull ( ̶ ̶ ̶) , Weibull-tailing ( ̶ · ̶ ) and Biphasic ( —) model.

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

gl)

Time (min)

(A)

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

gl)

Time (min)

(B)

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

gl)

Time (min)

(C)

*LOQ = 2.40 log10 copies/g

66

Figure 3-5. Thermal inactivation curves of MS2 at 60 (A); 72 (B) and 90°C (C) in mussel matrix fitted with Log linear-tailing (…), Log linear-shoulder-tailing (xxx), Weibull-tailing ( ̶ · ̶ ), Two-mixed Weibull (═), Biphasic (—) and Biphasic-shoulder (○○○) model

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

gl)

Time (min)

(A)

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

gl)

Time (min)

(B)

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250

Log 1

0(N

)(log

cop

ies/

gl)

Time (min)

(C)

*LOQ = 0.70 log10 PFU/g (---)

67

3.3.4. The z curves of NoV and MS2 thermal inactivation.

The calculated D, 2D and 4D values from each thermal inactivation model of NoV and MS2 in

buffered media and mussel matrix are presented in Table 3-3 and 3-4. The calculated D values from

the best fitted of three models (which has the lowest RMSE for non-linear models or the closest

coefficient of determination (R2) to 1 for linear models) (Table 3-3 and 3-4) were plotted against the

temperature of the treatment to generate a general secondary model (z curves) of thermal

inactivation (Figure 3-6). For comparison to the general z curves, specific secondary models of

Biphasic (Figure 3-7 and 3-8) were derived from D values of Biphasic model only.

In general, the modified Log linear and Weibull with tailing models were failed to calculate the 4D

values from thermal inactivation of NoV but were successful for MS2. As expected in this study, the

fastest time to reduce 4 log concentrations (4D value) of the viruses in buffered media and mussel

matrix were observed from heating at 90°C for less than 1 min.

68

Table 3-3. The predicted time to log reduction at D, 2D and 4D and the calculated RMSE values from the thermal inactivation curves of NoV in different matrices fitted by Log Linear, Weibull and Biphasic models.

Initial Conc. Matrix Temp.

(°C)

Model fitting Log Linear Weibull Biphasic

Time to log reduction (mins) RMSE Curves



D 2D 4D D (n=1) 2D 4D D(Dinitial) 2D 4D

6.27 ± 0.16 log

copies/ml

Buffered Media

60 30.83 84.29 n/a 0.405 Tailing 15.04 93.04 575.80 0.296 Normal 16.75 101.43 399.85 0.301 Normal 72 4.71 10.32 n/a 0.415 Tailing 1.28 11.45 102.98 0.172 Normal 2.25 12.05 60.21 0.218 Normal 90 2.29 4.68 n/a 0.541 Tailing 0.24 2.27 21.54 0.303 Normal 0.57 1.50 19.91 0.352 Normal

6.64 ± 0.17 log

copies/g Mussel

60 24.74 51.46 n/a 0.511 Tailing 11.09 66.14 393.11 0.587 Normal 20.68 45.39 573.05 0.519 Normal 72 4.27 8.88 n/a 0.359 Tailing 2.06 7.89 n/a 0.313 Tailing 3.66 8.10 99.73 0.356 Normal 90 2.58 5.50 n/a 0.590 Tailing 1.21 7.18 42.58 0.485 Normal 0.77 4.19 24.01 0.397 Normal

Note: The D value predicted from the best fitted models (with the lowest RMSE value) were written in bold and used to create z curves.

Table 3-4. The predicted time to log reduction at D, 2D and 4D and the calculated RMSE values from the thermal inactivation curves of MS2 in different matrices fitted by Log Linear, Weibull and Biphasic models.

Initial Conc. Matrix Temp. (°C)

Model fitting Log Linear Weibull Biphasic

Time to log reduction (mins) RMSE Curves Time to log reduction

(mins) RMSE Curves Time to log reduction (mins) RMSE Curves

D 2D 4D D (n=1) 2D 4D D(Dinitial) 2D 4D

7.89 ± 0.07 log PFU/ml

Buffered Media

60 21.13 42.43 n/a 0.564 Tailing 6.41 31.06 150.34 0.237 Normal 10.43 22.04 153.69 0.127 Normal 72 1.35 2.63 5.39 0.692 Tailing 0.39 1.61 6.78 0.290 Tailing 1.29 2.58 5.27 0.394 Normal 90 1.08 2.16 4.32 0.593 Tailing 0.04 0.31 2.67 0.482 Normal 0.36 1.43 2.91 0.574 Normal

7.80 ± 0.03 log PFU/g Mussel

60 39.61 67.93 125.89 0.114 Shoulder-tailing 39.01 66.32 142.13 0.117 Double-

Weibull 40.40 65.16 139.70 0.102 Shoulder

72 2.92 5.82 12.09 0.579 Tailing 1.05 4.25 17.23 0.421 Tailing 1.97 3.99 17.80 0.228 Normal 90 1.15 2.30 4.64 0.549 Tailing 0.16 0.82 4.11 0.245 Tailing 0.76 1.52 3.33 0.338 Normal

Note: The D value predicted from the best fitted models (with the lowest RMSE value) were written in bold and used to create z curves.

69

The general z curves (Figure 3-6) showed that temperature and matrix type affected the D values of

NoV and MS2. The intercept values of the curves (calculated from the log linear regression curves) in

mussel matrix was always higher than in buffered media for both NoV and MS2 (data not shown). In

addition, the predicted D values in buffered medium were constantly lower than in mussel matrix at

temperature more than 50°C by a constant proportion (Figure 3-6). The D values of NoV were

generally higher than MS2 in both buffered medium and mussel matrix, for each temperature

studied. Furthermore, when the best fitted models were used to predict the D, 2D and 4D values,

the inactivation in mussel matrix required more time, except for MS2 in mussel heated at 60°C

(Table 3-3 and 3-4), showing that the NoV and MS2 were more susceptible to heat treatment in

buffered media than in mussel. A similar trend was also observed from the specific z curves (Figure

3-7) generated from the D values of the Biphasic model, where NoV has higher predicted D values

than MS2, and thermal inactivation in mussel required more time than in buffered medium, at each

temperature studied.

70

Figure 3-6. Predicted general z curves in buffered media (—) and mussel matrix (…) of NoV (A) and MS2 (B) in buffer (▲) and mussel matrix (□) at different temperatures.

Under the assumption that the matrix effect is constant for each temperature, the calculated D

values of MS2 was better predicted by the general z curves (Figure 3-6), while for the NoV, the

calculated D values from the Biphasic model (Figure 3-7) produced a better prediction.

y = 35214e-0.135x

R² = 0.9507

y = 12440e-0.111x

R² = 0.8765

0.0

0.1

1.0

10.0

100.0

0 20 40 60 80 100

Tim

es re

quire

d to

a lo

g 10

redu

ctio

n (m

in)

Temp (°C)

(A)

y = 415134e-0.183x

R² = 0.9554

y = 1E+06e-0.18x

R² = 0.9711

0.0

0.1

1.0

10.0

100.0

0 20 40 60 80 100

Tim

es re

quire

d to

a lo

g 10

redu

ctio

n (m

in)

Temp (°C)

(B)

71

Figure 3-7. Predicted specific z curves in buffered media (—) and mussel matrix (…)of NoV (A) and

MS2 (B) in buffer (▲) and mussel matrix (□) at different temperatures.

3.4. Discussion

Enteric viruses that caused foodborne diseases are often linked to three categories of food, i.e. filter

feeder shellfish (bivalve mollusc), raw products contaminated with water containing viruses, and

meals or foods prepared by infected food handler (Deboosere et al., 2004b). Thermal inactivation

including cooking, pasteurization, sterilisation, canning and blanching has been widely applied in

food production systems to reduce or eliminate pathogenic bacteria and viruses (Bertrand et al.,

2012; Richards et al., 2010; Teixeira, 2015), thus the determination of D and z values became key

ybuffer = 9367.2e-0.11x

R² = 0.951

ymussel = 11384e-0.108x

R² = 0.9792

0.0

0.1

1.0

10.0

100.0

0 25 50 75 100

Tim

es re

quire

d to

a lo

g 10

redu

ctio

n (m

in)

Temp (°C)

(A)

y = 5352.4e-0.109x

R² = 0.9367

y = 44461e-0.126x

R² = 0.843

0.0

0.1

1.0

10.0

100.0

0 25 50 75 100

Tim

es re

quire

d to

a lo

g 10

redu

ctio

n (m

in)

Temp (°C)

(B)

72

elements in measuring heat resistance of microorganism during thermal inactivation process

(Holdsworth et al., 2016; Van Asselt & Zwietering, 2006). In the past 40 years, thermal inactivation

has been evaluated for its efficacy to reduce HAV, rotavirus, PV and, NoV and its surrogates in

shellfish (Abad et al., 1997; Araud et al., 2016; Bozkurt et al., 2014b; DiGirolamo et al., 1970; Hewitt

& Greening, 2006; Millard et al., 1987). However, the use of predictive modelling to predict the D

values of the virus in thermal inactivation studies especially in shellfish has just started in the 2010’s

(Araud et al., 2016; Bozkurt et al., 2015a; Bozkurt et al., 2014b; Park & Ha, 2015; Park et al., 2014).

None of these studies, however, were directly compared the predicted D values of NoV and its

surrogate, and/or utilised MS2 as NoV surrogate for the inactivation studies.

Predictive inactivation models of NoV and MS2 as its surrogate in different temperatures and

matrices were evaluated in this study. The heat treatment at 60, 72 and 90°C mimicked to cooking

process of stir-frying, steaming and boiling, respectively. By utilising both linear and non-linear

models to fit the viral inactivation curves in this study, the survival data of both viruses during

thermal treatment appeared to be best fitted by Weibull or Biphasic than the log linear. This finding

is in agreement with some previous studies (Araud et al., 2016; Bozkurt et al., 2013, 2014a) which

suggested that Weibull or Biphasic model produced a better fit of thermal inactivation kinetics of

NoV and its surrogate, with lower RMSE values than the linear model. Although the Weibull model

was appropriate to present the thermal inactivation curves, however this model was unsuccessfully

to predict a complete NoV elimination for extended contact time (outside the full duration of the

treatment) in both matrices. Thus in this study, only the Biphasic model can be used to predict the

required time to complete elimination of NoV. Based on the predicted inactivation curves from

Biphasic model (Figure 3-2 and 3-4), for example, a complete elimination of NoV in buffered media

and mussel matrix can be achieved after heating at 90°C for approximately 40 and 50 min,

respectively.

This present study observed the tailing phenomenon in all curves generated from the inactivation

data in both matrices (Figure 3-2 to 3-5). Similar observations were shown from the previous viral

73

inactivation studies in suspension or shellfish matrix (Araud et al., 2016; Bozkurt et al., 2013; Bozkurt

et al., 2015a; Escudero-Abarca et al., 2014; Tuladhar et al., 2012), where tailing phenomenon was

present during thermal inactivation. This phenomenon can be hypothesised due to the presence of

subpopulations that have a different response toward thermal treatment. The tailing can be caused

by the slow reduction of a subpopulation, such as the aggregated viral fraction (Langlet et al., 2007;

Tuladhar et al., 2012) or the protected viral particles attached inside the tissue that were more

resistant than other subpopulations outside the tissue towards environmental changes due to high

content of fat and protein in the tissue (Bidawid et al., 2000). Viral aggregation is potentially

occurred due to the changes of the environmental conditions (such as the presence of salts, cationic

polymers or suspended organic matters) (Gerba & Betancourt, 2017). Hence, it is worth noting that

the use both an aggregated and non-aggregated viral particle in the future studies of inactivation by

heat treatment is necessary.

The suitability of MS2 as a NoV surrogate for thermal inactivation study was evaluated in this study

by comparing the D, 2D, 4D as well as the z values of NoV and MS2 predicted from the best fitted of

three models (Log linear, Weibull and Biphasic). As observed, NoV was generally more resistance to

heat than MS2 in both matrices. NoV presented higher z values as well as the D, 2D and 4D values

than MS2 in each heating treatment, except for 60°C treatment in mussel (Table 3-3 and 3-4). For

example, the z values of NoV and MS2 from thermal inactivation in mussel were 20.75° and 12.79 °C,

respectively, and the D values of NoV and MS2 in buffered media at 72°C were 1.28 and 0.39 min,

respectively. These observations show evidences that MS2 may not suitable to be used as NoV

surrogate to describe the heat resistance of NoV toward thermal inactivation in buffered medium.

However, when comparing these results with result from other studies, the thermal resistance of

MS2 in suspension at 72°C was similar to HAV (Hewitt et al., 2009), but higher than FCV and MNV-1

(Bozkurt et al., 2014a; Cannon et al., 2006). The predicted D value of MS2 from this study was 0.39

min, while the D values of HAV, FCV and MNV-1 at 72°C were ≤0.30, between 0.10 to 0.12 and 0.09

to 0.17 min, respectively. Hence, MS2 is more relevant to represent the heat-resistance of HAV than

74

NoV towards thermal inactivation in the suspension, and is potentially to be used as a HAV

surrogate.

This present study also evaluates the matrix effect on thermal inactivation by comparing the D, 2D

and 4D values of the viruses, predicted from the best fitted of three models in buffered media and

bioaccumulated mussel. The differences in thermal resistance of NoV or MS2 in buffered media and

in mussels were observed in this study. The D, 2D and 4D values of NoV or MS2 in mussel matrix

were higher than in buffered media, except for the D values of MS2 in mussel at 60°C treatment

(Table 3-3 and 3-4) where shoulder phenomenon was observed during the first 70 min of contact

time (Figure 3-5 A). The time differences to obtain certain log reductions of the virus in buffered

media and mussels indicates the occurrence of matrix effect during thermal inactivation, in which

NoV or MS2 were more resistance to heat in complex than in simple matrix. This finding is in

consistency with result from previous study by Park and colleagues (2014), the virus (MNV-1) was

more resistance to heat in complex matrix (dried mussels) than in the simple matrix (culture

medium/suspension) at 60, 85 and 100°C treatment which was shown by the higher D values in

dried mussels than in suspension. Moreover, similar trend was also observed from a study by Croci

and colleagues (2012), where the number of infectious of NoV and FCV from heating at 80°C for 3 to

15 min were higher in complex matrix (spiked mussels) than in simple matrix (viral suspension).

Possible explanation for this matrix effect is that the mussel contains protein and fat which could

protect the viral particle from the heat (Bozkurt et al., 2014b) and prevent viral aggregation (Croci et

al., 2012).

3.5. Conclusions

Overall, this study presents tailing phenomena during thermal inactivation of NoV and MS2, which

due to the occurrence of heat-resistant subpopulation. Thus, non-linear models (Weibull and

Biphasic) were more appropriate than linear model (log linear) to describe the inactivation kinetics

of both viruses. The Biphasic model was also more suitable than Weibull to predict virus survival for

75

extended contact times (outside the full duration of the treatment), when two or four log reductions

are considered as the thermal inactivation objective. The thermal inactivation kinetics were affected

by different matrices, where complex matrix such as mussel provided higher protection for the viral

particles against heat treatment than the simple matrix (buffered media). It is worth noting that MS2

can be used as NoV surrogate to describe this phenomenon, but caution should be taken when

extrapolating the MS2 inactivation kinetics for NoV inactivation studies because MS2 is less resistant

than NoV toward thermal treatment.

76

Chapter 4. Chlorine dioxide inactivation of NoV and MS2 in buffered media

and artificially contaminated Tasmanian Blue Mussels (Mytilus

galloprovincialis) tissue

4.1. Introduction

Consumption of raw or improperly cooked shellfish has been identified as a major cause of of NoV

infection (Alfano-Sobsey et al., 2012; Maunula & Von Bonsdorff, 2014). Bitler et al. (2013) suggested

that the attack rate (which is defined as the number of cases per 100,000 persons exposed to NoV

contaminated food) in shellfish was the highest amongst other type of foods (produce and ready to

eat foods). Food in general are contaminated by NoV through different routes, such as contact with

infected food handlers or cross-contamination during food processing (Hall et al., 2012); or contact

with NoV-contaminated water at their harvesting/growing sites during production (Bellou et al.,

2013; Polo et al., 2015; Rodríguez-Lázaro et al., 2012). While contamination of NoV into water

environment can be caused by several factors, such as sewage leak, surface contamination due to

heavy rainfall or flooding, water treatment (chlorination) failure and water system breakdown

(Maunula, 2007). Therefore, the use of untreated contaminated-water for food processing and

handling could contribute to NoV contamination in food.

In the case of potentially transmission of NoV during the food processing, the implementation of

GHP and the application of disinfectants and sanitizers play important role in reducing the

contamination (Barker et al., 2004; Boxman, 2013). Many studies have highlighted the potential

application of disinfectants to reduce viral contamination during food processing and to be used as a

cleaning agent for the processing facilities, particularly using NoV surrogates (D'Souza & Su, 2010;

Feliciano et al., 2012; Fraisse et al., 2011; Grove et al., 2015; Malik & Goyal, 2006; Takahashi et al.,

2011). Among other disinfectants, these studies showed that chlorine-containing compounds such

77

as sodium hypochlorite, chloramines and chlorine dioxide (ClO2) have effectively reduced viral

contamination.

Chlorine-containing compounds have been considered and reviewed by the expert panels of

FAO/WHO as potential disinfectants used in food production and processing globally (FAO & WHO,

2009) and have been widely used as disinfectants in the cleaning and sanitation steps of seafood

processing (Huss, 1994). For instance, chlorine (one of these compounds) is a common disinfectant

added into water which is used for different purposes, including to wash the fish, to make the ice for

chilling the fish, to thaw the frozen fish or to cool the canned fish after retorting (FAO & WHO,

2000). Hypochlorite is also used to decontaminate containers and table surface in the fish processing

industry in Indonesia with concentration ranges from 20 to 100 mg/l of total chlorine (FAO & WHO,

2009). From Indonesia perspective, the use of chlorinated-water in fish production lines in Indonesia

is regulated through the Decree of Ministry of Marine and Fisheries Affairs (MMAF) KEP

01/MEN/2002 about the Intensive Quality Management System of Fishery Product (MMAF

Indonesia, 2002), where chlorine can be added into water as a disinfectant for washing purpose at

the maximum of 10 mg/l of total chlorine (MMAF Indonesia, 2002). Moreover, the free chlorine

residue in the water to be used in fish processing should not exceed 5 mg/l (Ministry of Health

Indonesia, 2010).

The efficacy of chlorine-containing compounds to inactivate and to reduce enteric virus such as NoV

and its surrogates (e.g., FCV, MS2 phage, MNV and PV-1), has been investigated and evaluated

(Feliciano et al., 2012; Kim et al., 2012; Kitajima et al., 2010; Montazeri et al., 2017; Rachmadi et al.,

2018; Sigstam et al., 2014; Tung et al., 2013). Results from these studies showed that the difference

in disinfectants efficacies to reduce and to inactivate viruses were observed. The variability in the

disinfectant efficacies of those studies were being influenced by some parameters used during the

inactivation, such as: the differences in mode of inactivation, types and concentration of the

disinfectant, contact time and virus species.

78

Another factor that may influence the disinfection efficacy is the differences in disinfection decay

rate (k’ values) (Haas & Joffe, 1994; Shin & Sobsey, 2008) which occurs when different types of

chlorine-containing compounds (such as hypochlorite, chloramines and ClO2) and different modes of

inactivation that are being used (Gómez-López et al., 2009). The efficacy of chlorine-containing

compounds as disinfectant is also influenced by pH, temperature and the presence of organic matter

(Hirneisen et al., 2010; Kingsley et al., 2014; Morino et al., 2009; Tung et al., 2013).

In particular, previous studies that evaluated the efficacy ClO2 to reduce the NoV and its surrogates

were only performed in suspension or buffered media, produce or fruit matrices and in hard

surfaces, and rarely compared it with meat matrix, especially shellfish (Girard et al., 2016; Kingsley et

al., 2018; Lim et al., 2010; Montazeri et al., 2017; Morino et al., 2009; Yeap et al., 2016). Compared

to the matrices used in those studies, shellfish has different composition of both organic and

inorganic compounds. As a consequence, the application of ClO2 as disinfectant in the shellfish

matrix may represent a different efficacy than the result from the previous studies on chlorine-

containing compounds disinfection. Hence, investigating the efficacy of ClO2 as disinfectant in

shellfish matrix is required.

The efficacy of the treatment is commonly assessed by calculating the concentration of ClO2 over the

time (C) and the decay rate (k’) values using the first-order kinetic, and followed by predicting the

inactivation kinetics using the Hom model (Haas & Joffe, 1994; Hornstra et al., 2011). This approach

has been widely used to calculate the efficacy of chlorination as well as ClO2 treatment to reduce

microbial and viral contamination in water treatments (Cromeans et al., 2010; Haas & Joffe, 1994;

Hornstra et al., 2011; Jacangelo et al., 2002; Kahler et al., 2010; Murphy et al., 2014). Another

model such as the modified biphasic can also be used for the comparison of the inactivation kinetic

and to describe the tailing phenomenon during the ClO2 inactivation in drinking water (Hornstra et

al., 2011).

79

In our study, the efficacy of ClO2 treatment to reduce NoV and MS2 bacteriophage were evaluated in

both buffered media and artificially-contaminated mussel. Pre-treatment RT-qPCR was used to

enumerate the infectious NoV from the treatment, while plaque assay method was used for the

quantification of MS2. In the same ClO2 treatment, the reliability of MS2 bacteriophage as a NoV

surrogate was also assessed by comparing the inactivation kinetic of both viruses in the same matrix,

while the matrix effect was evaluated by comparing inactivation kinetic of the virus in buffered

media and mussel. The quasi-mechanistic Hom, Weibull and Biphasic model were used to calculate

the inactivation kinetics of both viruses during the treatment, while the first-order kinetic equation

was used to determine the decay rate of ClO2.


4.2.1. Mussels preparation and artificial contamination.

Five kilograms of live Tasmanian Blue Mussel (Mytilus galloprovincialis) were purchased from a

single local fish market/supplier. Mussel acclimatisation and depuration were done as described in

Section 3.2.3 for 6 h and by changing the sterilized sea water every 2 h. One hundred pieces of tissue

mussels were taken out from the shells and were pre-washed with sterile saline water (0.3% NaCl) at

4°C. Artificial contamination of the mussel was done by dipping the tissue in NoV and MS2 solutions

at a final concentration of approximately 108 copies/ml and 108 PFU/ml, respectively, for 30 min. The

tissues were then drained for 60 min at 4°C to remove the excessive solution. The dipping method

was done to provide NoV contamination at the shellfish tissue surface (not inside the tissue) which

mimicked the process of viral cross-contamination by secondary transmission.

4.2.2. Chlorine dioxide treatments

Chlorine dioxide (ClO2) stocks (5,000 ppm) were prepared following procedure from Cleanoxide®

(NaturalWater Solutions, Australia) by mixing 1 part of solution A and 9 part of solution B. The

mixture was shaken for 15 s and stored at a dark glass bottle for 8-10 h in 4°C to complete the

80

reaction. The concentration of total ClO2 stock was determined by the DPD-based method (Palin,

1957) using the Palintest Chlorometer ClO2+ Kit (Palintest, Australia). This kit was able to quantify

only chlorine dioxide in the sample. The ClO2 stock was serially diluted to make 250; 500; and 1,000

ppm of working solutions. The ClO2 treatments were performed in two different matrices i.e.,

buffered medium (PBS) and mussel matrices at different ClO2 concentration (10; 20 and 40 ppm) for

certain period of times as shown in Table 4-1. The treatment of each concentration in both matrices

were done in triplicate and carried out in 50 ml plastic tube incubated at water bath to maintain the

temperature at 20°C. For the treatment in buffered media, tube containing 45 ml of PBS-ClO2

suspension were prepared by adding the ClO2 working solutions into the 40 ml PBS solution in the

tube to obtain the final concentrations of 10; 20; and 40 ± 1 ppm. A five ml of virus stock containing

NoV and MS2 at concentration of 107 copies/ml and 108 PFU/ml, respectively, were added into the

tubes. The concentration of ClO2 was measured immediately following procedure from the

manufacture (Palin Test Kit, Australia) after certain exposure time (Table. 4-1). For the ClO2

treatment in mussel matrix, 5 g of contaminated-mussel was dipped into the plastic tube containing

45 ml ddH2O-ClO2 and exposed to the treatment for certain periods of time, as shown in Table 4-1.

After each exposure time, the ClO2 concentration was measured immediately, and the mussels were

transferred to a new tube and added with 1 ml of 10% sodium thiosulfate to inactivate the

remaining ClO2. The sample was then concentrated and purified as described in Section 4.2.4.

Table 4-1. Exposure time of ClO2 treatment at different concentrations

ClO2 concentration (ppm)

Σ treatment tubes Exposure time (min)

Buffered medium Mussel

0 (Initial/No treatment) 3 3 0 10 21 21 1; 10; 20; 30; 40; 50; and 60 20 21 21 1; 10; 20; 40; 60; 80; and 100 40 21 21 1; 20; 40; 80; 120; 160; and 200

81

4.2.3. Analysis of ClO2 residue by Palintest kit

The remaining ClO2 in the suspension after each exposure time was quantified using the Palintest Kit

(Australia) according to the manufacturer’s instructions without any modifications.

4.2.4. Virus and bacteriophage purification

The infectious viruses from the treatment in mussel matrix were purified following the procedure of

Lewis and Metcalf (1988) and Mullendore et al. (2001), with modifications as previously described in

Section 3.2.6.1 of this thesis, while the infectious viruses in buffered media were directly processed

for subsequent plaque assay (for MS2) or pre-treatment and RNA extraction (for NoV) without the

purification step.

4.2.5. Enumeration of MS2 by plaque assay

The infectious MS2 from the treatment was enumerated using a double layer agar method (EPA,

2001), with modifications, as previously described in Section 2.2.2.2.a of this thesis.

4.2.6. Virus pre-treatment and RNA extraction

Prior to nucleic acid extraction, the purified sample was pre-treated using RNase as previously

described in Section 2.2.4 of this thesis. Subsequently, RNA samples was extracted using method of

Chomczynski and Sacchi (2006), with modifications, as previously described in Section 2.2.2.2.b of

this thesis

4.2.7. Enumeration of NoV by RT-qPCR

For the enumeration of infectious NoV GII, the RT-qPCR were performed using method of

Jothikumar et al. (2005) with modifications as previously described in Section 3.2.6.3 of this thesis.

82

4.2.8. Modelling and statistical analysis of ClO2 inactivation kinetics

As described from previous study by Haas and Joffe (1994), the decay rate of ClO2 during inactivation

process was calculated using a first-order kinetic equation as follows.

𝐶𝐶 = 𝐶𝐶0𝑒𝑒−𝑘𝑘′𝑑𝑑 (Equation 4-1)

where C and C0 are ClO2 residue (mg/l) at time t and time 1 min (closest measurement to time zero),

respectively, and k’ is the first-order ClO2 decay rate constant (min-1). The k’ value for each

experiment were calculated using the Solver function in Microsoft Excel 365 (Microsoft Corp).

The infectious NoV and MS2 from each treatment were fitted into Hom, Weibull and Biphasic

models. The Hom model equation as previously described by Thurston-Enriquez et al. (2003) and

Haas and Joffe (1994) is written as follows.

𝐿𝐿𝑛𝑛 � 𝑑𝑑𝑑𝑑0� = −𝑘𝑘𝐶𝐶0𝑘𝑘𝑚𝑚 𝑘𝑘 �1 − 𝑒𝑒(−𝑛𝑛𝑘𝑘′𝑑𝑑/𝑚𝑚)/(𝑛𝑛𝑘𝑘′𝑘𝑘/𝑘𝑘)� (Equation 4-2)

where Ln (N/N0) is the natural log of the survival ratio of virus (number of viruses remaining at time t

(N) divided by the average of initial number of viruses without treatment (N0)). The k value is the

Hom inactivation rate constant, n is the dilution coefficient, and m is an empirical constant that

describes the deviation from ideal Chick-Watson kinetics (Sigstam et al., 2014). The Solver function

in Microsoft Excel 365 (Microsoft Corp.) was used to determine the values for each model’s

coefficients by minimising the sum of squares of the difference between the observed and predicted

of natural log reduction over the time (Ln(N/N0)) for viral inactivation with the same virus and

matrix. Inactivation curves of NoV and MS2 (log reduction over the time (min) (Log10(N/N0)) were

also calculated and created using Microsoft Excel 365 (Microsoft Corp.).

Weibull (Eqn. 3-3) and Biphasic model (Eqn. 3-5) previously described in Section 3.2.7 were used to

predict the log reduction value over the time (Log10(N/N0)) for each experiment with the same virus

and matrix. The inactivation curves of the models were fitted and calculated using GInaFiT for

Microsoft Excel (Geeraerd et al., 2005). The coefficient of determination (R2) was used to evaluate

83

the predicted C values compared to the observed, while the Root Mean Square Error (RMSE) values

was used to measure the goodness-of-fit of the decay rate and the inactivation models.

The single factor of Analysis of Variance (ANOVA) was carried out to calculate the differences of the

calculated k’ rate and the observed log10 reductions of NoV and MS2 using Real Statistics Add-ins for

Microsoft Excel 365 (Microsoft Corp).

4.3. Results

4.3.1. ClO2 decay in buffered media and mussel matrix

The residue of chlorine dioxide (ClO2) (at pH 6.9 ± 0.2) of each treatment at 20 ± 1°C over the

inactivation period in buffered media is shown in Figure 4-1. The ClO2 residue analysis was done

using Palintest kit with the detection limit (LOD) of 0.02 ppm. The residue values were plotted

against the exposure time to produce ClO2 decay curves. In general, ClO2 concentration decreased

over time during the treatment. These curves were then fitted using the first-order kinetics model to

calculate the initial concentration residue (C0) and the decay rate (k’) of ClO2. The calculated (C0) in

the solution at 1 min exposure (the closest measurement to time zero) were 8.40, 19.25 and 30.13

ppm for treatment with 10, 20 and 40 ppm, respectively. The k’ of ClO2 during treatment were

varied between 0.052 to 0.056 min-1, with the average of 0.053 ± 0.023 min-1. The R2 between the

observed and the predicted C values for 10; 20; and 40 ppm treatment were above 95% for each

treatment with the RMSE values of 0.516; 0.834; and 2.338, respectively.

84

Figure 4-1. The observed () and predicted (---) values of ClO2 residue (C) (from (a) 10, (b) 20, and (c) 40 ppm treatment at 20°C for different exposure times in buffered media.

Figure 4-2 describes the effect of mussel matrix on ClO2 concentration over the time. A decrease in

ClO2 concentration was observed in all cases, similar to the apparent effects in the buffered media.

However, the ClO2 decay in mussel matrix showed higher k’ value than in the buffered media. The

average k’ rate in mussel (0.080 ± 0.0024 min-1) was significantly higher (p<0.01) than in buffered

media (0.053 ± 0.0023 min-1). Moreover, the observed initial ClO2 concentrations (C0) of 10, 20 and

40 ppm treatment in mussel matrix (7.40, 12.51 and 17.86 ppm, respectively) were lower than in

buffered media. Similar observation to the ClO2 decay curves in buffered media, the R2 values

between observed and predicted C in those three curves (10; 20 and 40 ppm) in mussel matrix were

more than 95% with the RMSE value of 0.513, 0.606, and 1.335, respectively. In addition, the

observed residue values of ClO2 from all treatments in both matrices (buffered media and mussel)

after 60 min exposure were less than 5 ppm.

0

2

4

6

8

10

0 10 20 30 40 50 60

ClO

2re

sidu

e (p

pm)

Contact Time (min)

C=8.401e-0.051872t

R2 =0.964RMSE = 0.516

(a)

0

5

10

15

20

25

0 20 40 60 80 100

ClO

2re

sidu

e (p

pm)

Contact Time (min)

C=19.255e-0.05156t

R2 =0.982RMSE = 0.834

(b)

0

5

10

15

20

25

30

35

0 40 80 120 160 200

ClO

2re

sidu

e (p

pm)

Contact Time (min)

C=30.128e-0.05569t

R2 =0.964RMSE = 2.338

(c)

85

Figure 4-2. The observed () and predicted (---) values of ClO2 residue (C) from (a) 10, (b) 20, and (c) 40 ppm treatment at 20°C for different exposure times in mussel matrix.

4.3.2. The efficacy of ClO2 treatment on NoV and MS2 in buffered media

The infectious NoV in buffered media treated with different concentrations of ClO2 at 20 ± 1°C were

enumerated using pre-treatment RT-qPCR assay with LOQ at 250 copies/ml (2.40 log10 copies/ml). To

calculate the reductions of MS2, the infectious MS2 in buffered media after exposure to ClO2

treatment were analysed using plaque assay (LOQ at 20 PFU/ml). The observed log10 reductions of

the viruses (log10(N/N0)) were plotted against the contact times to generate inactivation curves. In

general, the viral inactivation curves observed in this study showed a tailing shape, thus non-linear

models were better to describe the viral inactivation by ClO2. The inactivation curves of NoV and

MS2 in buffered media fitted using non-linear models i.e., Hom, Weibull and Biphasic models were

shown in Figure 4-3 and 4-4. Since the Hom model produced the reductions in natural logarithm

value (Ln(N/N0)), therefore the value of log reductions (log10(N/N0)) was obtained by extrapolating

the value of Ln(N/N0) using Microsoft Solver Add-in.

0

2

4

6

8

0 10 20 30 40 50 60

ClO

2re

sidu

e (p

pm)

Contact Time (min)

C=7.398e-0.08135t

R2 =0.955RMSE = 0.51253

(a)

0

2

4

6

8

10

12

14

0 20 40 60 80

ClO

2re

sidu

e (p

pm)

Contact Time (min)

C=12.5076e-0.08037t

R2 =0.984RMSE = 0.606

(b)

0

5

10

15

20

25

30

0 40 80 120 160 200

ClO

2re

sidu

e (p

pm)

Contact Time (min)

C=17.857e-0.0768t

R2 =0.976RMSE = 1.335

(c)

86

Table 4-2. The RMSE and R2 values of the ClO2 inactivation models of Hom, Weibull and Biphasic

Matrix Virus Initial viral conc. (N0)

ClO2 (ppm) Inactivation model

Hom Weibull Biphasic RMSE R² RMSE R² RMSE R²

Buffered media

NoV 6.39 ± 0.20 log10 copies/ml

10 0.121 0.922 0.131 0.922 0.123 0.935 20 0.316 0.793 0.349 0.789 0.324 0.830 40 0.451 0.856 0.511 0.852 0.473 0.884

MS2 7.37 ± 0.11 log10 PFU/ml

10 0.128 0.960 0.139 0.959 0.142 0.960 20 0.330 0.922 0.369 0.917 0.305 0.946 40 0.397 0.955 0.722 0.871 0.355 0.971

Mussel

NoV 6.59 ± 0.44 log10 copies/g

10 0.145 0.725 0.193 0.582 0.161 0.725 20 0.261 0.641 0.283 0.640 0.273 0.685 40 0.213 0.830 0.232 0.827 0.223 0.848

MS2 6.40 ± 0.07 log10 PFU/g

10 0.172 0.784 0.195 0.761 0.177 0.813 20 0.139 0.945 0.192 0.907 0.136 0.956 40 0.211 0.895 0.261 0.861 0.212 0.913

Note: The RMSE values written in bold indicate the lowest RMSE produced by the best-fitted model.

Hom’s showed the lowest RMSE values amongst the other models in predicting the log10 reductions

value of NoV for each treatment (Table 4-2), thus these predicted log reductions values of Hom’s

were used to describe the treatment efficacy in NoV. The highest NoV reduction predicted in

buffered media treated with ClO2 for 60 min was observed from the 40 ppm ClO2 with 3.05 log10

reductions, while treatment at 10 and 20 ppm were predicted to reduce NoV numbers by 1.61 and

2.38 log10 reductions, respectively. In contrast with the NoV inactivation data, Biphasic model was

observed as the best fitted model (giving the lowest RMSE values) to predict the log10 reductions of

MS2 inactivated by 20 and 40 ppm ClO2 in buffered media (Table 4-2.), while Hom only gave the best

prediction at 10 ppm treatment. Hence, the predicted log10 reductions of MS2 in 20 and 40 ppm

treatment were calculated using Biphasic model, while the predicted log10 reductions of 10 ppm

treatment was calculated using Hom model. Compared to NoV, similar observation was shown on

the efficacy of ClO2 treatment, where the higher ClO2 concentration produced the higher value of

estimated log reductions. However, MS2 were more susceptible towards ClO2 treatment, where the

log10 reductions of MS2 in the same treatment for the same time exposure were higher than NoV.

The predicted reductions of MS2 in 10, 20 and 40 ppm were 2.46, 4.02 and 5.18 log10 reductions,

respectively.

87

Figure 4-3. The log reductions (Log10(N/N0)) curves of NoV in the buffered media fitted using Hom (…), Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆)ppm ClO2 for

different exposure times

Figure 4-4. The log reductions (Log10(N/N0)) curves of MS2 in the buffered media fitted using Hom (…), Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆) ppm ClO2 for


4.3.3. The efficacy of ClO2 treatment on NoV and MS2 in mussel matrix

The viral inactivation curves by ClO2 in the mussel matrix are presented in Figure 4-5 and 4-6. In the

mussel matrix experiment, Hom model produced better prediction on the viral reduction treated

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0 40 80 120 160 200

Log 1

0(N

/N0)

Time (Min)

-7

-6

-5

-4

-3

-2

-1

0

0 40 80 120 160 200

Log 1

0(N

/N0)

Time (Min)

88

with 10, 20 and 40 ppm ClO2 than Weibull or Biphasic model, except for MS2 treated with 20 ppm

ClO2 (where the biphasic was the best-fitted model for this treatment) (Table 4-2). The predicted

reduction values of NoV in mussel treated with 10, 20 and 40 ppm of ClO2 for 60 min were 1.14, 1.38

and 1.43 log10 reductions, respectively. These values were lower than the MS2 reductions toward

similar treatments, except in the 10 ppm treatment. The MS2 reduction treated with ClO2 at

concentration of 10, 20 and 40 ppm for 60 min were 1.09, 1.66 and 1.72 log10 reductions.

Figure 4-5. The log reductions (Log10(N/N0)) curves of NoV in the mussel fitted using Hom (…), Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆) ppm ClO2 for


Although the predicted log10 reductions of NoV was slightly higher than MS2 in the 10 ppm

treatment for 60 min in mussel matrix, however, the result from analysis of variance (ANOVA)

showed that there were no significant differences (P>0.05) between the average of observed log10

reductions of NoV (≈1.07 ± 0.07 log10 reductions) and MS2 (≈1.08 ± 0.12). From the observed data,

the maximum reduction of NoV and MS2 in mussel matrix were achieved by treated using 40 ppm

ClO2 for 200 min. The average of NoV and MS2 reductions observed from these treatments were

1.94 ± 0.33 and 2.17 ± 0.19 log10 reductions, respectively (Figure 4-5). In overall, MS2 were more

susceptible than NoV toward ClO2 treatment in the mussel matrix which were similar to the

-3

-2.5

-2

-1.5

-1

-0.5

0

0 40 80 120 160 200

Log 1

0(N

/N0)

Time (Min)

89

observed trend in the experiment using buffered media. This finding indicated that the MS2 is not

the best candidate for a NoV surrogate in this study since the number of infectious MS2 does not

represent the infectious NoV from the treatment.

Figure 4-6. The log reductions (Log10(N/N0))curves of MS2 in the mussel fitted using Hom (…), Weibull (---), and Biphasic model (—) treated with 10 (▲), 20 (○), and 40 (◆) ppm ClO2 for


In this study, the matrix effect was observed during ClO2 inactivation for both viruses at

concentration of 10, 20 and 40 ppm. The observed maximum log reductions of NoV and MS2 after

exposed to ClO2 for certain periods in buffered media were significantly higher (P<0.05) than in

mussel matrix as presented in Table 4-3. For example, the average of NoV and MS2 reduction in

buffered media after 100 min treated with 20 ppm were 2.49 ± 0.16 and 4.62 ± 0.29 log10 reductions,

respectively, where only 1.53 ± 0.56 and 1.80 ± 0.21 log10 reductions were observed in the mussel

matrix.

-3

-2.5

-2

-1.5

-1

-0.5

0

0 40 80 120 160 200

Log 1

0(N

/N0)

Time (Min)

90

Table 4-3. The average of observed maximum reduction of NoV and MS2 treated by ClO2 exposed for certain periods

Virus Treatment The average of log10(N/N0)

(log10 reductions) Buffered Media Mussel

NoV 10 ppm for 60 min 1.60 ± 0.25a 1.07 ± 0.07b 20 ppm for 100 min 2.49 ± 0.16a 1.53 ± 0.56b 40 ppm for 120 min* 3.76 ± 0.35a 1.65 ± 0.23b

MS2 10 ppm for 60 min 2.49 ± 0.16a 1.08 ± 0.12b 20 ppm for 100 min 4.62 ± 0.29a 1.80 ± 0.21b 40 ppm for 200 min 6.08 ± 0.01a 2.17 ± 0.19b

Note: *The exposure time of 120 min was used in the 40 ppm treatment, as some missing data was observed in the exposure of 160 and 200 min

The same letter in the same row denotes no significant differences (p>0.05)

4.4. Discussion

The likelihood of viral transmission to human from the ingestion of food contaminated by enteric

viruses were reported from some foodborne outbreaks. Although the major sources of enteric viral

contamination in food originated from the main transmission route where the food has been

directly contacted with faecal-contaminated water (Bellou et al., 2013), for example in the NoV or

HAV contamination in shellfish, some types of food have been reported to be contaminated by

enteric viruses through secondary transmission via cross-contamination during food handling.

Findings from previous studies showed that some enteric viruses and their surrogate can be

transmitted into the food through the contact with food handlers’ hand, washing water and the

equipment during handling and processing in fresh produce, fruit and ready-to-eat meals (Dalton et

al., 1996; Grove et al., 2015; Holvoet et al., 2014; Maunula et al., 2013; Schmid et al., 2007). During

the viral cross-contamination in food, viral particles were commonly attached in the food surfaces

(Todd et al., 2009), thus this contamination can be reduced or eliminated by the application of

disinfectant in washing step. In addition, the use of disinfectants as control strategies in GHP and

GMP regime have been widely applied in food industries, including for fisheries product (FAO &

WHO, 2009).

91

In fish processing industries, ClO2 is generally used to improve the application of hygienic practices,

rather than in the decontamination procedure (FAO & WHO, 2009). The efficacy of this compound to

reduce the level of pathogenic bacteria has been studied in oyster (Shin et al., 2004) and other

seafood products (salmon, grouper, scallops, and shrimps) (Kim et al., 1999) as well as in

antimicrobial ice used in fish processing (Wang et al., 2010). These studies suggested that ClO2 can

be used as an effective bacterial disinfectant in fish and oyster with the minimum concentration of

20 ppm. However, studies that evaluate the efficacy of this compound against viral contamination in

seafood are still limited. Hence in the current study, the efficacy of ClO2 treatment with various

concentration from 10 to 40 ppm was evaluated to reduce NoV in buffered medium and mussel

matrix.

As previously described in Chapter 1 and 2, NoV is the most common cause of NoV infection and the

challenge to cultivate this virus makes it difficult to perform the quantification using a cell culture

system. Therefore, the used of cultivable surrogates such as MNV, FCV and MS2 have been widely

proposed to overcome this problem and to understand the inactivation mechanism of NoV. In this

study, the efficacy of ClO2 (at 20 ± 1°C with pH 6.9 ± 0.2) to reduce NoV and MS2 using identical

experiment condition in two different matrices (buffered media and mussel) was evaluated and

compared. The experiment in mussel matrix was designed to understand the efficacy of ClO2 to

reduce viral particles contaminated the mussel in which cross-contamination scenario was applied,

hence the artificial contamination of the virus in this study was performed by dipping the mussel’s

tissue into viral stock.

Results from this current study showed that the decay of ClO2 in both matrices was observed during

viral inactivation. The ClO2 decay rates were constant following the model of first-order-kinetic with

the R2 values of >95% in all concentrations observed. The matrix effect in ClO2 decay was also

observed in the inactivation of both viruses (NoV and MS2) where the ClO2 decay rate in mussel was

faster than in buffered media (solution). The possible explanation of this matrix effect is that the

mussel tissue contains more organic and inorganic compounds than the buffered media. Thus the

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available chlorine including ClO2 were being consumed faster for oxidation, addition and

electrophilic substitution reactions of these compounds (Deborde & von Gunten, 2008) in the

mussel matrix than in buffered media. To maintain the ClO2 concentration during inactivation

treatment, a closed reactor (pump) as used by (Sigstam et al., 2014) can be suggested in future

inactivation experiment in both matrices.

The virucidal effects of ClO2 (as disinfectant) in NoV and MS2 were investigated in this study. In viral

inactivation studies using disinfectants, a temporary inactivation could occur due to a reversible

change in the virus conformation, while damage on the capsid protein and/or nucleic acid may

resulted in the permanent inactivation of the virus (Thurman & Gerba, 1988).The efficacy of ClO2 to

inactivate viral particles varied depend on the virus species as well as the matrix used in the

experiment. In general, MS2 was more susceptible than NoV towards ClO2 treatment especially in

buffered media. The discrepancies in the effectivity for viral inactivation by chlorine-containing

compounds were also observed from previous studies when different viruses were used for the

identical treatment in their studies (D'Souza & Su, 2010; Duizer et al., 2004; Dunkin et al., 2017;

Kitajima et al., 2010; Montazeri et al., 2017; Shin & Sobsey, 2008; Sigstam et al., 2013). Generally,

the virus stability depends on the capsid structure to provide protection from environmental stress

(Hirneisen et al., 2010; Nuanualsuwan & Cliver, 2003), thus different capsid structure has different

mechanisms toward environmental stress which affect their persistence in the environment and

their sensitivity to disinfectants (Cook et al., 2016; da Silva et al., 2007; Seitz et al., 2011; Verhaelen

et al., 2013).

It is worth noting that each virus species or strain has a different structure of capsid protein and

genome, thus it has a different response toward disinfectant such as chlorine-containing compounds

(Sigstam et al., 2013; Wigginton et al., 2012). In addition, from the extensive investigation of the

viral inactivation mechanism using MS2, Wigginton and colleagues (2012) suggested that even the

same virus species may have different susceptibilities toward environmental stress. As consequence,

the observation from the current study together with those previous studies that highlighted the

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differences in susceptibility of NoV and MS2 toward ClO2 raise a concern about the compatibility of

MS2 as NoV surrogate. Caution must be considered when utilising inactivation kinetic data of MS2

for NoV inactivation by ClO2.

The inactivation curves of NoV and MS2 in buffered media and mussel matrices from this study

showed tailing phenomena. Further investigation of this phenomenon is important since its

occurrence might indicate incomplete inactivation of the targeted microorganisms (Sigstam et al.,

2014). The tailing phenomena of inactivation curves could be explained by the consumption of ClO2

over exposure time, as suggested by other studies (Lim et al., 2010; Sigstam et al., 2014). This

condition was particularly supported by the ClO2 decay rate observed from the current study, which

showed similar pattern as the inactivation curves. Another possible explanation of the tailing

phenomena is the occurrence of mixed population with different susceptibilities against ClO2.

Hornstra et al. (2011) advised that certain attachment process to particles or different disinfectant

reactions that occur during treatment might instigate the presence of subpopulation within the

original MS2 population. Furthermore, viral aggregation or viral clumping in the suspension was also

proposed as a condition which could lead to the tailing phenomena (Thurman & Gerba, 1988). Viral

aggregation could inhibit the effect of disinfectant because the consumption of disinfectant in the

outer layer of viruses which leave only smaller concentration of disinfectant to react with the viruses

in the inner layer (Mattle et al., 2011). Thus, viruses in the inner layer will be inactivated slower than

the outer part. This aggregation is often referred as a protective mechanism of core virion against

disinfectant. Besides, quantification of each single non-infectious viral particle is not possible to be

done in the aggregated virus, thus the number of infectious virus appears constant (Sigstam et al.,

2014) and observed as tails.

Based on the evaluation of ClO2 efficacy to inactivate NoV in the current study, this compound was

able to reduce NoV in the simple (buffered media) and complex matrix (mussel). Nevertheless, the

direct application of ClO2 to reduce NoV contamination in mussel matrix might not provide sufficient

reductions when the reduction objective of a treatment is set at more than 2 log10 reductions. Using

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disinfectant solution might be sufficient to eliminate microbial contamination at the food surface,

however it might be ineffective to remove the virus that have penetrated inside the food matrix

(Richards, 2001), such as a natural viral contamination in shellfish through the bioaccumulation

process. Therefore, it can be suggested that ClO2 is more suitable to be used as disinfectant to

reduce viral contamination in the surface of matrix, for example: in the water used for washing the

food handler’s hand and cleaning the processing equipment, and washing or cleaning the surface of

raw shellfish.

The highest NoV reduction in this study was achieved from 40 ppm ClO2 after 120 min treatment in

buffered media and mussel matrix, with the ClO2 residue of less than 2 ppm. This ClO2 concentration

used in this study were within the range of concentration considered (5-100 ppm) by the FAO/WHO

expert meeting to rinse, wash, thaw, transport and stored fish products (FAO & WHO, 2009).

Moreover, the recommended ClO2 residue as disinfectant for these purposes by Food and Drug

Administration of the United States (USFDA) (CFR 173.300) is less than 3 ppm (USFDA, 2018).

The current legislation on the shellfish sanitary program in Indonesia controlled the used of chlorine

in fish processing (MMAF Indonesia, 2002), while such regulation for ClO2 has not available yet.

Therefore, results from this current study can be used as input for future assessment of ClO2 as

disinfectant in fish processing practices especially for stakeholders and government in Indonesia,

since this method could be used as a control strategy in shellfish processing to prevent any potential

secondary contamination of NoV.

4.5. Conclusion

ClO2 can be used as a candidate disinfectant in the processing of fishery product. At a concentration

of 40 ppm for 120 mins treatment, ClO2 gave 3.76 ± 0.35 log10 reduction of NoV in buffered media

but only 1.65 ± 0.23 log10 reduction was obtained in mussel matrix. Thus, this disinfectant is more

suitable to be used as a washing or cleaning sanitizer which could prevent secondary and cross-

contamination of NoV during handling and processing.

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Furthermore, MS2 was more susceptible than NoV towards chlorination, however the used of this

surrogate is recommended to understand the kinetic mechanism of NoV in the inactivation studies.

Future studies could be improved by using a closed reactor to control the ClO2 concentration during

treatment and by using different type of surrogates.

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Chapter 5. Risk assessment of NoV GII in shellfish from Indonesian fish

markets

5.1. Introduction

Genogroup I and II of NoV are known as human NoV and are food contaminants that can cause

human gastroenteritis (Lees, 2000; Scallan et al., 2011; Torok, 2013). NoV are the etiologic agents

responsible for 68% of acute gastroenteritis outbreaks from 1999 to 2010 in the US (Hall et al.,

2013). Verhoef et al. (2015) studied worldwide infections due to NoV from 1999 to 2012, and

reported that almost 14% of all the outbreaks are associated with food as a primary source of

exposure (with other common sources including sewage contamination and exposure in child care

centres, aged care homes, and cruise ships). An epidemiological study of gastroenteritis outbreaks in

Europe from 1995 to 2000 (Lopman et al., 2003) found that NoV, especially NoV genogroup II (NoV

GII) was the major causative agent of all non-bacterial outbreaks of human gastroenteritis. Such

studies demonstrate that NoV is an important source of human gastroenteritis outbreaks in many

countries.

In general, enteric viral contamination of foods occurs via the following routes of transmission:

direct contamination from human sewage and faeces/fomites, indirect contamination from infected

food handlers (also known as person-to-person contamination) and through zoonotic transmission

which involve animals (FAO & WHO, 2008; Verhoef et al., 2015). To elaborate, following an

outbreak, the infectious viral particles that were shed in the faeces or vomit of the infected person

can be transmitted back to the environment, especially in the water (Montazeri et al., 2015). These

suspended viral particles can remain in the water for several days to weeks while maintaining the

same level of infectivity (Brake et al., 2018). Therefore, aquatic organisms such as shellfish, which

tend to remain in the same contaminated water and filter the water to obtain food, are likely to be

the most susceptible to accumulation of viruses (Lees, 2000; Montazeri et al., 2015).

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The presence of NoV in shellfish from different markets, restaurants and harvesting areas in Asia

(Kittigul et al., 2016; Maekawa et al., 2007; Umesha et al., 2008), Europe (Boxman et al., 2006; Croci

et al., 2007; Le Guyader et al., 2009; Li et al., 2014; Loutreul et al., 2014; Lowther et al., 2010;

Lowther et al., 2012; Mesquita et al., 2011; Terio et al., 2010), the USA (Montazeri et al., 2015), and

Australia (Brake et al., 2014; Symes et al., 2007) has been reported. The presence of NoV in shellfish

has also been directly related to gastroenteritis outbreaks (Huppatz et al., 2008; Symes et al., 2007).

These reports emphasise the need to develop Quantitative Microbiological Risk Assessments

(QMRA) for NoV as a valuable tool to estimate, and optimally manage, human health risks associated

with the consumption of NoV-contaminated shellfish.

Shellfish are one of the most commonly consumed fisheries products in Indonesia. Generally,

shellfish in Indonesia are consumed in cooked condition such as boiled, steamed or stir-fried and are

mussels, clams or cockles. The Indonesian government recommends that shellfish in markets are

cooked to open the shell, before sale to consumers (BSN, 2009). When Indonesian consumers buy

raw (un-cooked) shellfish from the market, the pre-cooking step to open the shell is commonly done

at home before they cook the shellfish.(Anonymous, 2018) The consumption of raw shellfish such as

oysters has not yet become popular in Indonesia. In 2013, mollusc (including shellfish) production in

Indonesia reached 60,471 tonnes per year, with 23,611 tonnes are intended for domestic

consumption (FAO, 2015), while the major commodities being Green Mussels (Perna viridis), Clams

(Meretrix spp.) and Cockle (Anadara spp.) (Directorate General of Fisheries, 1999; Murdinah, 2009;

Setyono, 2007; WWF-Indonesia, 2015). These shellfish were mainly produced from growing areas in

fresh, brackish and marine water (Nurdjana, 2006). Some of the farming and harvesting sites are

located in bays and coastal waters close to human settlements, such as in Jakarta Bay, Lampung Bay

(Ali et al., 2015; Ferdinan, 2017; Noor, 2014; Sulvina, 2018) and Brebes (Prasetya et al., 2010; Rejeki

et al., 2016). As a result, some growing areas might be exposed to domestic sewage including faecal

pollution and therefore vulnerable to NoV contamination. Hence, there is a need to assess the risk,

e.g., the potential number of gastroenteritis cases due to the consumption of contaminated-shellfish

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by enteric viruses (especially NoV) from Indonesian fish markets to determine risk management

needs and options.

To ensure the quality and safety of shellfish harvested from growing areas, the Indonesian

government initiated a program in 2004 to undertake routine monitoring and surveillance of water

and shellfish quality in some shellfish growing areas (MMAF, 2004). Following this regulation, the

Indonesian government also issued a standard for processing for frozen (peeled) shellfish (SNI 3460-

2009) which consists of 11 handling steps (including the pre-cooking step). The pre-cooking step is

defined as a method to open the shellfish shell by placing the shellfish in boiling water until the

shells open and then cooling it immediately in clean water at maximum temperature of 5°C (BSN,

2009).

Although the microbiological quality of the water and shellfish are monitored, only faecal coliforms

are used as a faecal pollution indicator. No tests for enteric viruses are performed during this routine

monitoring. Therefore, information and data on enteric virus contamination (especially NoV) in

shellfish from Indonesian fish markets is unavailable. It is, thus, difficult to estimate the NoV risk

associated with shellfish consumption in Indonesia.

This study aimed to provide NoV prevalence data in shellfish obtained from Indonesian markets,

especially from Jakarta and Panimbang fish markets, and to develop a risk assessment for human

consumers from NoV in this commodity. This information can be used by the relevant competent

authority in Indonesia to develop regulations to ensure the safety and quality of shellfish in

Indonesian markets.


5.2.1. Sample collection from Indonesian fish markets in Jakarta and Panimbang.

Shellfish samples were collected from fish markets in two different cities, i.e. Jakarta and

Panimbang. In Jakarta, shellfish were purchased from two “traditional” fish markets (Cilincing and

Muara Kamal) and one “modern” fish market (Everfresh), as shown in the map in Figure 5-1. In

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Panimbang, shellfish were purchased from one traditional fish market. The term “modern market”

describes a hygienic fish market that follows the standard sanitation practices as defined by

regulations of the Indonesian Ministry of Marine Affairs and Fisheries (MMAF) (MMAF, 2017), while

the term “traditional” market describes a fish market that has not applied those standard hygienic

practices yet.

All shellfish purchased from traditional markets in Jakarta were harvested from Jakarta Bay, while

shellfish from Panimbang fish market were harvested locally from Panimbang and Labuan (Banten

Bay). However, samples from Everfresh fish market were supplied domestically and harvested from

other local farming sites in Indonesia (apart from Jakarta, Panimbang and Labuan).

Figure 5-1. Shellfish sampling locations in Jakarta and Panimbang

Triplicate individual samples of each shellfish species from each market were collected at three

times within three weeks in July 2016 and 2017. The DT were aseptically removed from the shellfish

samples and stored at -20°C. Viral particles were concentrated using PEG and pre-treated using

RNase enzyme as detailed in Section 5.2.2, below. The ribonucleic acids of the viruses were

extracted using Trizol (Invitrogen, USA) combined with the spin column method (Yaffe et al., 2012)

with modifications (described in Section 5.2.2), and preserved using 70% ethanol and transported to

100

the University of Tasmania within 2-3 days for further purification. The samples were kept at -20°C

during transportation.

5.2.2. Viral extraction and purification from shellfish digestive tissues

Viral particles were concentrated following protocols modified from Lewis and Metcalf (1988);

Mullendore et al. (2001). Briefly, two grams of shellfish DT were inoculated with 100 µl of

approximately 108 PFU/ml MS2 (as a process control to determine viral extraction efficacy) and

homogenized in a Waring blender for 30 s at high speed with 1:4 (wt/vol) 10% tryptose phosphate

broth (TPB) in 0.05 M glycine (pH 9.0). The suspension was then shaken at 250 rpm for 30 min at 4°C,

and centrifuged at 5,000 x g for 5 min. The remaining DT were collected and stored at -20°C for

further viral re-extraction (if the viral extraction efficiency of the sample was less than 10%). The

subsequent concentration steps were performed as previously described (Section 3.2.6) except that

for the final step of viral purification the pellet was re-suspended in 200 µl PBS, pH 7.5.

5.2.3. Plaque assay method to determine viral extraction efficiency

A hundred µl of the virus sample was analysed using plaque assay as previously described (Section

2.2.2) to determine the viral extraction efficiency. The viral extraction efficiency can be calculated as

the percentage of the number of MS2 after extraction divided by total added MS2 to the sample

before extraction. Following the approach of Le Guyader et al. (2009) only virus samples with a viral

(MS2) extraction efficiency more than 10% were used for further enzymatic pre-treatment and RNA

extraction as described in Section 5.2.4, below. Any virus sample with less than 10% extraction

efficacy was re-extracted following the previous procedure (Section 5.2.2).

5.2.4. RNase pre-treatment and RNA extraction

The viral suspension was subjected to RNase pre-treatment as previously described (Section 2.2.4).

The ribonucleic acid was extracted by guanidine-phenol-chloroform (Chomczynski & Sacchi, 2006)

followed by the spin column method (Yaffe et al., 2012) with modifications, as follows. In brief, 100

µl viral suspensions isolated from shellfish samples were mixed with 1 ml Trizol reagent in 1.5 ml

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microtubes. Two hundred µl of chloroform:isoamylalcohol (24:1 v/v) was then added to the sample

and mixed up and down for 15 sec. The suspension was centrifuged at 12,000 x g for 10 min at 4°C

and the aqueous phase was then transferred to new microtubes containing 500 µl isopropanol and

10 µl of 1mg/ml glycogen (Sigma Aldrich, USA). This sample was incubated for 2 hours at -20 °C and

then centrifuged 12,000 x g for 10 min at 4°C. The supernatant was discarded, and the pellet was

dissolved in 350 µl GuSHCl buffer. The suspension was then added to an equal volume of 70%

ethanol, mixed well and stored at -20°C. In subsequent extraction steps, the mixture was transferred

to a spin column (Qiagen, Germany) and centrifuged at 8,000 x g for 30 s at 4°C. The eluate was

discarded, and the column was washed three times: once with 500 μl 3 M Na-acetate and then twice

with 500 μl 70% ethanol to remove salts. Between and after washes, the column was centrifuged at

8,000 x g for 30 s at 4°C and the eluate collected and discarded. The column was ‘dried’ by

centrifugation at 7,000 x g for 2 min at 4°C. For elution of the RNA from the column, 50 μl of DEPC-

treated water at 60°C were added directly to the column membrane, incubated for 2 min at room

temperature and centrifuged at 8,000 x g for 2 min at 4°C. The eluate, containing the nucleic acid,

was kept and stored at -70°C.

5.2.5. Enumeration of NoV by RT-qPCR

Due to unavailability of GI standard plasmid in Tasmanian Institute of Agriculture (TIA) laboratory,

only NoV GII assay was performed using RT-qPCR protocols previously described (Section 3.2.6.3)

and was done in duplicate per sample as confirmation step to avoid a false positive and negative

result. The negative result is defined as a sample with NoV concentration below the LOD value. Only

the highest NoV concentration from each positive sample was used for further study. The LOD and

LOQ of this assay were determined following MIQE guidelines for real-time PCR assay (Bustin et al.,

2009) and suggestion by Forootan et al. (2017).

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5.2.6. Statistical analysis

A chi-squared test was used to analyse whether the different sources and species influenced the

amount of NoV contamination in the shellfish, while Analysis of Variance (ANOVA) and the Duncan

Test were used to assess the significance of differences in NoV contamination level among species.

These calculations were performed using Microsoft Excel and Real Statistics Resource Pack add-in,

and SigmaPlot ver.12.5 (Systat Software Inc., UK).

5.2.7. Genotyping

The genotype of all NoV GII (i.e., samples > LOD) detected from the shellfish samples was

determined by sequencing using a CEQTM 8000 Genetic Analysis (Beckman Coulter System, USA) at

the Molecular Laboratory of the Central Science Laboratory, University of Tasmania. Sequences of

NoV GII ORF1-ORF2 junction region were amplified by RT-nested PCR as previously described by

Kageyama et al. (2003) using G2FB and G2SKR as forward and reverse primers, respectively. The

alternative primer sequences in this study, i.e., NOV-G2-BP-F (5'-GCC CCA ATC ATG AAG ACC CA-3’)

as forward and NOV-G2-BP-R (5'-CAC CTG GAG CGT TTC TAG GG-3') as reverse primers, were

designed using Primer-BLAST NCBI that amplify 475 bps sequence of RdRp and capsid genes (nt

sequence from 4,830 to 5,304 bps which cover ORF 1 region, ORF1-ORF2 junction and ORF 2 region).

These primers used when the PCR product could not be amplified using Kageyama’s method due to

primer mismatch with the RNA template especially in ORF1 region. The PCR products from the gel

electrophoresis were purified using a QIAquick Gel Extraction Kit (Qiagen, Germany) and sequenced

using GenomeLab DTCS – Quick Kit (Beckman Coulter, USA) according to the manufacturer’s

instructions. Sequences were analysed and corrected using BioEdit Alignment Editor (Hall, 1999).

The sequences of PCR products were aligned with the published sequences from Gen Bank database

using the NCBI-BLAST (Basic Local Alignment Search Tool) available at

https://blast.ncbi.nlm.nih.gov/Blast.cgi. Phylogenetic analysis was performed using MEGA 6

software (Tamura et al., 2013).

https://blast.ncbi.nlm.nih.gov/Blast.cgi

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5.2.8. Quantitative risk assessment of NoV in shellfish from Indonesian markets

A risk assessment (RA) was performed following the principles and guidelines for microbiological risk

assessment (MRA) established by the Codex Alimentarius Commission (CAC), including a structural

approach that consists of hazard identification, hazard characterization, exposure assessment and

risk characterisation (FAO & WHO, 2001). A point-estimate model that determining some “worst-

case” scenarios was used to develop the risk assessment. This model employs a single number of

each data set which is used as an input in the risk calculation (Lammerding & McKellar, 2004;

Zwietering & Nauta, 2007), for example mean of prevalence, highest level of contamination or

average of shellfish consumption. This deterministic approach is a suitable model for developing the

quantitative risk assessment of NoV in shellfish from Indonesian fish markets due to the paucity of

some Indonesian data inputs such as NoV outbreak cases, incidences of NoV illness associated with

shellfish consumption, and the proportion of shellfish species consumed by the Indonesian

population. The data for NoV inactivation by thermal inactivation (Chapter 3 of this thesis) was used

to calculate the potential NoV reduction after handling and cooking of the commodities.

The current regulation concerning processing of frozen shellfish in Indonesia (SNI 3460-2009)

requires boiling the shellfish in the boiled water until the shells open (assumed as heating at 90-

100°C for approximately 3-4 min) (Hewitt & Greening, 2006)), before the shellfish can be sold in the

market (BSN, 2009). This pre-cooking method is considered in this study, and its effectiveness to

reduce the risk of illness is estimated and compared with the non-pre-cooking method.

To estimate the risk of NoV, the dose per shellfish serving for different marketed shellfish (pre-

cooked or non-pre-cooked) and various formats of shellfish consumed (i.e., boiled, steamed and stir-

fried) per consumer were calculated using Equation 5-1 and 5-2. These equations were developed in

this study based on the combination of previous dose equations from Tenuis and colleagues (1997)

and Pintó and colleagues (2009), and were adjusted with the variety of assumptions and the worst-

case scenarios used in this study (for details see Table 5-1). The calculated dose was then used to

estimate the probability of illness (P*ill) per consumer according to the exponential model as

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previously described by Teunis et al. (1997) (Equation 5-3). The estimated number of NoV cases (N)

based on the marketed shellfish formats (i.e., pre-cooked and non-pre-cooked) and the specific

cooking method of consumed shellfish (i.e., boiled (Nb), steamed (Ns) or stir-fried (Nf)) per year was

then calculated using the equation developed in this study (Equation 5-4). Moroever, the number of

total NoV cases per year due to the assumption of ‘mixed’ cooking methods (NM), can be calculated

as the sum of Nb, Ns and Nf (NM = Nb + Ns + Nf). The mixed cooking method was assumed as the

combination of boiling, steaming and stir-frying method in equal proportion used to cook the

shellfish by Indonesian consumer. Parameters involved in these equations are detailed in Table 5-1.

The NoV dose per serving when pre-cooking and non-pre-cooking step was applied to the marketed

shellfish is also calculated using Equation 5-1 and 5-2, respectively.

𝐷𝐷𝑙𝑙𝐷𝐷𝑒𝑒 = 𝑃𝑃 × 𝐶𝐶 × 𝑝𝑝 × 1/𝑅𝑅 × 𝐼𝐼 × 10−�𝑝𝑝𝑟𝑟𝑟𝑟+𝐿𝐿𝑜𝑜𝐿𝐿�𝑑𝑑𝑑𝑑0� �� × 𝑊𝑊 (Equation 5-1)

𝐷𝐷𝑙𝑙𝐷𝐷𝑒𝑒 = 𝑃𝑃 × 𝐶𝐶 × 𝑝𝑝 × 1/𝑅𝑅 × 𝐼𝐼 × 10−�𝐿𝐿𝑜𝑜𝐿𝐿�𝑑𝑑𝑑𝑑0� �� × 𝑊𝑊 (Equation 5-2)

𝑃𝑃 ∗𝑝𝑝𝑙𝑙𝑙𝑙= 1 − 𝑒𝑒−𝑟𝑟×𝐷𝐷𝑜𝑜𝑠𝑠𝑟𝑟 (Equation 5-3)

𝑘𝑘 = 𝑅𝑅𝑃𝑃 × 𝐶𝐶𝑅𝑅 × 𝑃𝑃 ∗𝑝𝑝𝑙𝑙𝑙𝑙 (Equation 5-4)

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Table 5-1. The parameter utilised in the risk assessment to estimate the dose per serving, the probability of illness and the number of NoV cases per year

Parameter Description Unit Reference Note

P The prevalence of NoV in shellfish from Indonesian fish markets % This study The average prevalence (as worst-

case scenario)

C The highest NoV concentration in the contaminated DT shellfish copies/g DT This study The maximum NoV concentration

(as worst-case scenario)

p The proportion of DT from the total weight of shellfish tissue % (Grodzki et al., 2014) The maximum proportion (as

worst-case scenario)

R The average recovery of the extraction method % This study The minimum value (as worst-case scenario)

I Proportion of infective viral particles among the detected viruses % This study The maximum proportion (as

worst-case scenario)

pre The viral log reduction due to the pre-cooking step Log10 reductions (Hewitt & Greening, 2006) The minimum value of viral log10 reductions (as worst-case scenario)

Log(N/N0) The log reduction of NoV by thermal inactivation processes that mimic the food processing styles (i.e., boiling, steaming and stir-frying)

Log10 reductions Chapter 3 of this thesis Log(N/N0) of NoV at 60, 72 and 90°C treatment was applied

W The average of shellfish consumption portion per consumer gram (Makmur et al., 2014)

r The dose response of NoV illness viral particle or genomic copies (Teunis et al., 2008)

Pop The total population of Indonesia of the year people (BPS-Statistics Indonesia, 2018)

SC The average of shellfish consumption per capita in Indonesia of the year gram (BPS-Statistics Indonesia,

2018; FAO, 2015)

Con The annual of total shellfish consumed in Indonesia gram This study (Con=Pop x SC)

S The expected (potential) contaminated servings servings This study (S= (P x Con)/W)

CM The proportion of shellfish consumption based on the consumption format % This study Assumption – no relevant data

available

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5.3. Results

5.3.1. NoV exposure from shellfish from Indonesian fish markets

Ninety shellfish samples including Green Mussel, Blood Cockle and Oriental Hard Clam (Figure 5-2),

were collected in 2016 and a further 81 samples were collected in 2017, from four different

Indonesian fish markets in Jakarta and Panimbang, i.e., Cilincing, Kamal, Everfresh and Panimbang

market. Some species, e.g., Oriental Hard Clam and Green Mussel, were not available for sampling in

Everfresh and Panimbang market as detailed in Table 5-2.

Table 5-2. The numbers of shellfish samples from Jakarta and Panimbang fish markets in 2016 and 2017

Sampling site

Market Year

Σ samples (per species) Σ samples (per site per

year) Name Type Oriental Hard Clam

Blood Cockle

Green Mussel

Jakarta Cilincing Traditional 2016 9 9 9 27

2017 9 9 9 27

Kamal Traditional 2016 9 9 9 27

2017 9 9 9 27

Everfresh Modern 2016 n/a 9 9 18

2017 n/a 9 9 18 Panimbang Panimbang Traditional 2016 6 6 6 18 2017 n/a 9 n/a 9

Total 42 69 60 171 *Note: n/a =not available in the market at the time of sampling

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Green Mussels (Perna viridis) Blood cockles (Anadara granosa)

Oriental Hard Clams (Meretrix lusiora)

Figure 5-2. Shellfish species collected from Indonesian fish markets

5.3.1.1. The efficiency of virus extraction and RNase pre-treatment process

To evaluate the efficiency of the virus extraction process from DT samples, MS2 was added as a

process control. The average extraction efficiency was analysed by comparing the calculated number

of MS2 (PFU/g) in the virus samples added before and those enumerated after the viral extraction

(% recovery). Following each market sampling in 2016 and 2017 (Section 5.2.1), three individual

samples from the approximately 30 samples were randomly picked and analysed by plaque assay to

evaluate the efficiency of viral extraction. The average efficiency of this extraction procedure and

RNase pre-treatment varied between 17.70 and 30.35% per batch (Table 5-3).

Table 5-3. The average extraction efficiency of MS2 as a control per batch

Batch No. Week Year

Recovery (%) Average

1 1 2016 30.35 ± 17.70 2 2 2016 31.06 ± 15.53 3 3 2016 27.01 ± 11.50 4 1 2017 21.87 ± 10.28 5 2 2017 17.70 ± 9.18 6 3 2017 24.33 ± 9.53

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5.3.1.2. NoV prevalence and enumeration in the shellfish from Indonesian fish markets

Positive samples were defined as shellfish that were contaminated with NoV at a concentration

above the limit of detection (LOD) (10 copies/g or 1 log10 copies/g DT) with no non-specific

amplification products (as determined by melt curves analysis in the RT-qPCR). The average

proportion of positive DT in Blood Cockles, Oriental Hard Clams and Green Mussels varied between 5

to 10% of total tissue weight (data not shown). The average NoV prevalence in shellfish from 2016

and 2017 sampling periods was 5.55% and 7.41%, respectively. The highest prevalence of NoV GII

was found in Green Mussels (10%), followed by Oriental Hard Clams (7.14%) and Blood Cockles

(2.9%) (Table 5-4), and all of the positive samples (>LOD) originated from “traditional” fish markets

in Jakarta (Table 5-5). No positive samples were detected in the Clam samples in 2016, but 3 positive

samples were found in 2017. The chi-square analysis showed that the NoV prevalence between

shellfish species was not significantly different (P>0.05) (Table 5-4) but was significantly different

between market sources (P<0.05) (Table 5-5).

Table 5-4. NoV prevalence in the shellfish samples from Indonesian fish markets according to species

Species Year (Positive/Total Samples)

Total Prevalence (%) 2016 2017

Oriental Hard Clam (Meretrix lusoria) 0/24 3/18 3/42 7.14a

Blood Cockle (Anadara granosa) 1/33 1/36 2/69 2.90a

Green Mussel (Perna viridis) 4/33 2/27 6/60 10a

*The same letter in the same column denotes no significant differences (P>0.05)

Table 5-5. NoV prevalence in the shellfish samples from Indonesian fish markets according to sampling sites

Sampling sites Market type Year (Positive/Total Samples)

Total Prevalence (%) 2016 2017

Jakarta Traditional 5/54 6/54 11/108 10.19a

Modern 0/18 0/18 0/36 0b

Panimbang Traditional 0/18 0/9 0/27 0b

*The same letter in the same column denotes no significant differences (P>0.05)

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The concentrations of NoV (GII) in the contaminated shellfish collected in 2016 and 2017 are

presented in Table 5-6. The LOQ of this assay is 20 copies/g or 1.3 log10 copies/g DT. The level of NoV

contamination in Oriental Hard Clam species was higher and significantly different (P<0.05) to Blood

Cockle and Green Mussels, however, there was no significant difference (P>0.05) between cockles

and mussels.

Table 5-6. NoV concentration in contaminated shellfish at traditional markets in Jakarta according to species

Species NoV (log10 copies/g DT)

For each positive sample Average

Oriental Hard Clam (Meretrix lusoria)

2.71

3.14±0.70a 2.78

3.95

Blood Cockle (Anadara granosa)

1.60 1.89±0.41b

2.18

Green Mussel

(Perna viridis)

1.43

2.07±0.51b

1.54

1.92

2.48

2.48

2.57

*LOQ of RT-qPCR is 1.30 log10 copies/g sample. The same letters in the same column denotes no significant differences (P>0.05)

5.3.2. Genotyping of NoV GII isolated from contaminated shellfish

A total of eleven NoV-positive samples were analysed for genotyping study. Samples were amplified

by a conventional RT PCR using G2FB and G2SKR primers following the method of Kageyama et al.

(2003) to produce 479 bps fragment. Only one of 11 samples was successfully amplified (with cDNA

concentration of <25 ng/µl), and was later identified as genotype GII.4 (sample C2C3) (Figure 5-3).

The alternative primers designed in this study were used to amplify the fragment from the ORF1,

ORF1-ORF2 junction and ORF3 regions from the remaining positive samples that could not be

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amplified by Kageyama’s method. Using these primers, another sample (K3C2) produced a 475 bp

fragment (with cDNA concentration of <25 ng/µl) and was also identified as genotype GII.4 (Figure 5-

3). In both genotyping processes, instead of applying cloning step, two primers from each PCR

method (Kageyama’s and alternative method) were used in the sequencing process to avoid noisy

area or poor sequences resolution due to a possible mixture of RNA from other GII strains in the

sample.

Figure 5-3. Phylogenetic tree of NoV GII detected from contaminated samples of Indonesian shellfish

5.4. Discussion

5.4.1. Prevalence and contamination levels of NoV in shellfish from Indonesian fish markets

This study presents the first data of NoV GII prevalence and contamination levels in some shellfish

species commonly purchased from Indonesian fish markets in Jakarta and Panimbang for human

consumption. The sampling sites were selected based on the market types (modern and traditional)

as well as the source of the marketed shellfish.

NoV GII.4_Dresden174/pUS-NorII/1997_AY741811.1

NoV GII.4_Osaka1/2007_AB541319.1

NoV GII.4_Farmington_Hills/2002_AY502023.1

NoV GII.4_Hunter504D/04O_DQ078814.2

NoV GII.4_Orange/NSW001P/2008_GQ845367.2

NoV GII.4_JB-15/KOR/2008_HQ009513.1

NoV GII.4 Huzhou_NS16439

NoV GII.4_Sydney_2012_isolate_15011203

NoV GII.4_isolate_Novosibirsk/NS16-A262/2016

Sample_1_Green Mussel_C2C3

Sample_5_Green Mussel_K3C2

NoV GII.6_strain_groundwater/GII-6/Deagu/2015/KR

NoV GII.3_strain_Hu/GII.3/AH15076/2015/CHN

NoV GII.2_isolate_Hu/GII.2/Tainan/16-7/2017/TW

NoV GII.5_strain_Hu/GII.5/GG(02169)/2010/KOR

NoV GII.21_GL02LP3

NoV GII.17_GL07LP6

NoV GII.1_isolate_Hu/GII.1/GM50/2017/CHN

0.05

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Shellfish from three traditional markets in Jakarta included shellfish harvested from more polluted

environments (Jakarta Bay), while shellfish from Panimbang (Banten Bay) market represented

shellfish from less polluted areas. As shown from the results of this study, the highest NoV

prevalence was found in Green Mussels from traditional markets in Jakarta. NoV was also found in

other types of shellfish from these markets but was not found in shellfish from Panimbang market,

which are mainly procured from Banten Bay. Banten Bay has been proposed by the Indonesian

MMAF as a potentially safer alternative to the shellfish growing sites in Jakarta Bay (Andriyanto,

2018) which are heavily exposed to industrial and domestic sewage (Dsikowitzky et al., 2016; Siregar

et al., 2016).

NoV GII.4 were identified from Green Mussels in this study. Genogroup GII, and especially GII.4, are

the most common cause of human gastroenteritis outbreaks (Bernard et al., 2014; Bull et al., 2006;

White, 2014). Thus, these findings emphasize the importance of regular monitoring and surveillance

of NoV in shellfish products. Although regular monitoring and surveillance is carried out by local and

national Indonesian authorities to ensure the safety and quality of shellfish in Indonesian markets,

information on NoV prevalence remains limited. The Indonesian government has mainly focused on

monitoring of biotoxins, heavy metals and bacterial contamination in shellfish products (MMAF,

2004), in which only the faecal coliform test was used as an indicator of faecal contamination in the

growing areas (BPLHD, 2015). If the results of routine monitoring indicate high levels of faecal

coliform contamination (> 300 MPN faecal coliforms/100 ml) in the water, and further laboratory

tests confirm these observations, the authorized agency is required to perform an evaluation of the

particular area and, as appropriate, the area will be declared as “off-limits” and no shellfish growing

and harvesting can be done in this area until the water quality improves and meets the requirements

(MMAF, 2004).

The use of faecal coliforms as an indicator for faecal contamination may not an effective approach to

assess NoV contamination of shellfish or harvesting sites. While the faecal coliform test can

effectively indicate enteric bacterial pathogens in shellfish and its production areas (Suffredini et al.,

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2014), it is less accurate to assess enteric virus contamination in the shellfish or viral dispersal in

sewage-contaminated water (Brake et al., 2018; Lee et al., 2013; Winterbourn et al., 2016).

5.4.2. Quantitative Risk Assessment of NoV in Shellfish from Indonesian markets

A Quantitative Microbiological Food Safety Risk Assessment (QMFSRA) utilising the NoV prevalence

and contamination levels data from this study was performed with different assumptions, including

‘worst-case scenarios’, (as described in Table 5-1) to provide scientific information on the risk of NoV

infections due to the consumption of shellfish in Indonesia. The QMFSRA was performed using a

deterministic approach following the guidance and example of the risk evaluation of viruses in

oysters in UK (ICMSF, 2018) and a model of HAV in shellfish (Pintó et al., 2009), with modifications to

the input parameters used for the risk calculations. The risk assessment in this study employed

various assumptions such as the application of a pre-cooking step to raw shellfish and also

considered the consumer behaviour toward shellfish cooking and consumption in Indonesia, where

the shellfish are usually consumed in the cooked form, i.e., after being boiled, stir-fried or steamed.

These pre-cooking and cooking practises could potentially eliminate or greatly reduce the

possibilities of NoV contamination in the final shellfish product. Hence the data on NoV thermal

inactivation (which mimics those cooking processes) from the Chapter 3 of this thesis were

integrated into the risk characterization.

5.4.2.1. Hazard identification

Occurrences of NoV in food as well as reported cases of NoV infection due to the consumption of

contaminated food in some developed countries have been well-reported (Lopman et al., 2003;

Scallan et al., 2011). In some developing countries such as Indonesia, however, foodborne

outbreaks caused by NoV are undocumented or underreported. The available published data on NoV

infections in some developing countries are limited to NoV prevalence from symptomatic and

asymptomatic patients such as in African (Armah et al., 2006; Ayukekbong et al., 2014), South

American (Bucardo et al., 2017; Fumian et al., 2016; García et al., 2006), and Asian countries

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including Indonesia (Nguyen et al., 2007; Sai et al., 2013; Subekti et al., 2002a; Subekti et al., 2002b;

Utsumi et al., 2017). In Indonesia, prevalence of NoV in patient stool samples varied between 2.7 to

20.6% (Subekti et al., 2002a; Utsumi et al., 2017). Although some NoV infections were recorded from

the patient, the information about the source of these infections were not available.

According to FAO data, mollusc (including shellfish) production in Indonesia increased from 53,684

to 60,471 metric tonnes in the period of 2010 to 2013 (FAO, 2015). However, some of the shellfish

growing and harvesting areas in Indonesia, such as Jakarta Bay, are located close to estuaries and

likely to be contaminated by domestic sewage from the surrounding settlement (Dsikowitzky et al.,

2016). As described earlier (Section 1.1.5), shellfish are highly susceptible to microbial

contamination, including viruses, due to their filter feeding behaviour (Le Guyader et al., 2013; Lees,

2000). Since NoV are highly persistent in the water environment (Brake et al., 2018; Cook et al.,

2016), shellfish grown in NoV contaminated areas are at high risk of being contaminated by NoV.

Current Indonesian national standards for shellfish products sold in the market provide guidelines on

safety and quality requirements as well as handling and processing of frozen (SNI 3460.1 to 3: 2009)

and canned shellfish (SNI 3919.1-3:2009). However, related regulations for fresh shellfish do not

exist, although this product is often sold fresh to consumers. Moreover, these regulations do not

consider viruses as potential microbial contaminants for raw material intended for frozen and

canned products.

5.4.2.2. Exposure assessment

Section 5.3.2. detailed the NoV prevalence from shellfish sold in Indonesian markets. A total of 171

samples were collected from four markets in Jakarta (Special Capital Region of Jakarta Province) and

Panimbang (West Java Province) in 2016 and 2017 (Table 5-2). As presented in Table 5-4 and Table

5-5, the average NoV prevalence was 6.48% and all positive shellfish were originated from

traditional markets in Jakarta. The shellfish sold in these markets are more likely to be grown in, or

harvested from, Jakarta Bay which has experienced environmental stress due to high loads of solid

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waste and wastewater from the surrounding households and industries from the city of Jakarta

(Dsikowitzky et al., 2016). Most of the wastewater is only partially treated or untreated and collects

into 13 rivers and canals which empty into Jakarta Bay (Nur et al., 2001).

Shellfish consumption data were estimated from the data of FAOSTAT (FAO, 2015) and the

Indonesian Ministry of Health (Indonesian Ministry of Health, 2014). FAOSTAT estimated that the

total domestic supply of molluscs intended for consumption in Indonesia in 2013 was 23,611 tonnes

(FAO, 2015). Furthermore, the national food consumption survey in Indonesia in 2014 suggested

that the number of consumer consuming squid and shellfish per year was 1.1% of the total

population (Indonesian Ministry of Health, 2014). This survey used a cross sectional design and was

conducted in every province in Indonesia. Consumer food intake during the last 24 h was recorded

from 191,524 participants from 51,127 households.

The average amount of shellfish consumed per serving is estimated as 185.29 g, derived from a

study in Cilincing, North Jakarta (Makmur et al., 2014). This survey involved 200 participants with

inclusion criteria as those who consume shellfish.

In the current study, due to unavailability of recorded or published data on the consumption of raw

shellfish in Indonesia, the proportion of shellfish consumed by Indonesian consumer was estimated

based on assumptions of different shellfish cooking methods. The most common cooking practices

of shellfish in Indonesia are boiling, steaming and stir-frying (Murdinah, 2009; Panjaitan et al., 2018;

Wongso & Tobing, 2012). Because there is lack of information and data about the proportion of the

different shellfish cooking methods, further assumptions were made on these proportions in this

current study (Table 5-7) to assess the importance of those assumptions on the risk estimates.

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Table 5-7. Assumptions on the proportion of shellfish cooked by different methods

Assumptions Percentage of cooking methods (%)

Boiling (90-100°C for 30 min)

Steaming (72°C for 15 min)

Stir-frying (60°C for 30 min)

1 100 0 0

2 0 100 0

3 0 0 100

4 (“mixed”) 33.3 33.3 33.3

The above proportions of cooking methods were estimated based on the antimicrobial potency of

each cooking method as well as the “mixed” method (assumption 4) to reduce NoV contamination in

the shellfish, thus the relative efficacy of different cooking method to reduce the risk of NoV cases

can be determined.

5.4.2.3. Hazard characterisation

Since specific studies on the dose response of Indonesian consumers (patients) to NoV exposure are

not yet available, the probability of infection in this study was calculated using the dose response

model developed by Teunis et al. (2008), while the NoV concentration and the serving size estimates

were provided in Table 5-6 and Section 5.4.2.2, respectively. Teunis’s model was derived from the

infectivity of NoV in human challenge studies, where the lD50 was estimated to be 1 million particles

or viral copies (ICMSF, 2018). The probability of illnesses due to the consumption of NoV

contaminated shellfish was estimated using a simple exponential model (Teunis et al., 1997).

To coordinate the available data with the required input values in this quantitative approach, several

assumptions were made. The NoV contamination level was analysed from the shellfish DT, which

comprise approximately 10% (assumed as the maximum proportion) of total shellfish tissue weight

(Grodzki et al., 2014). Although a previous study showed that the majority of the viral particles were

accumulated in the DT and were not homogenously distributed in other shellfish organs (McLeod et

al., 2009), however some other studies confirmed that viral particles were not only accumulated in

the shellfish DT but also in other organs such as gills, adductor muscle and haemolymph cell

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(Maalouf et al., 2010). Therefore, in this study the viral particles were assumed to be distributed not

only in the DT but also in other organs, as a worst-case scenario. This approach was applied to avoid

an underestimation of the dose calculated. The conversion factor (p) of 10%, which was obtained as

the proportion of digestive tissue from the total weight of shellfish tissue was used to calculate the

dose. The highest level of NoV contamination in shellfish from this study (8,980 copies/g DT before

adjustment by correction factor of recovery rate (R)) was also chosen as the worst-case scenario in

this risk assessment. Moreover, as there is no data available on the proportion of shellfish

consumption based on different species of shellfish, it was assumed that the population consumed

similar proportions of each shellfish species. A further assumption about the total shellfish (bivalve

molluscan shellfish) consumption was also made, since the FAO data on the total domestic supply

was calculated for molluscs in general (includes bivalve molluscan and other molluscs without shell).

All the required data and information to calculate the risk estimate are presented in Table 5-7.

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Table 5-8. Input parameters for the deterministic QRA to estimate the risk of NoV in shellfish from Indonesian fish markets

Parameter Values Reference(s) Note

P 6.48% Chapter 5 (Section 5.3.1) of this thesis Assumed at the average of annual prevalence

C 8,980 copies/g DT Chapter 5 (Section 5.3.1) of this thesis Worst-cases scenario

p 10% (Grodzki et al., 2014) Assumed at max. proportion

R 17.70% Chapter 5 (Section 5.3.1) of this thesis Worst-cases scenario

I 100% Chapter 5 (Section 5.4.2) of this thesis Worst-cases scenario

pre 2 (Hewitt & Greening, 2006) Worst-cases scenario

Log(N/N0) 4 (at 90°C for 30 min); 3 (at 72°C for 30 min); and 1 (at 60°C for 30 min) Chapter 3 (Section 3.3.4) of this thesis

W 185.9 g (Makmur et al., 2014)

r 1/1,000,000 copies or viral genomic (Teunis et al., 2008); (ICMSF, 2018)

Pop 248,800,000 (BPS-Statistics Indonesia, 2018)

SC 94.9 g (BPS-Statistics Indonesia, 2018; FAOSTAT, 2015)

Con 23,611,120,000 g - Calculated

S 8,230,234.41 servings - Calculated

CM Boiling; steaming; and stir-frying* - Assumed Note:*The details on the assumption of cooking methods proportion were described in Table 5-7

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5.4.2.4. Risk characterisation

Based on the thermal inactivation data from this thesis (Section 3.3.4) the reduction of NoV by a

cooking process such as boiling at 90-100°C for 30 min, steaming at 70-80°C for 30 min or stir-frying

at 60°C for 30 min were predicted to be at least 4, 3 and 1 log10 reductions, respectively. These log

reductions values of NoV in shellfish matrix due to thermal inactivation that mimicked the assumed

cooking process were estimated using a Biphasic (non-linear) model. In addition, the result from

previous study showed that >2 log10 viral reductions were achieved by boiling until the shell opens

(90°C for 3-4 min) (Hewitt & Greening, 2006). This value (2 log10 reductions) was used to determine

the minimum viral reduction achieved by the pre-cooking step (i.e., the worst-case). All of the viral

reduction values were then incorporated with the NoV annual prevalence and concentration data,

the average mass of shellfish consumed by the Indonesian population, the recovery rate of the

quantification method, the proportion of DT from the total shellfish body weight and the proportion

of virus infectivity in the sample, to estimate the doses of NoV per serve of shellfish (Equation 5-1

and 5-2).

By multiplying the estimated average probability of illnesses (P*ill) with the potential contaminated

servings, the annual NoV incidences based on the various assumptions of the most common shellfish

cooking methods in Indonesia with the worst-case scenarios were estimated. The results are

presented in Table 5-9. The annual attack rates of NoV (number of NoV-illness cases per 100,000

inhabitants per year) due to contaminated-shellfish consumption in Indonesia are presented in Table

5-10. These attack rates depend on the assumptions in the application of pre-cooking step of pre-

marketed shellfish as well as the cooking methods. For instance, when the pre-cooking step (boiling

at 90-100°C for 3-4 min) was incorporated into the risk calculation, the attack rates of each cooking

methods were, as expected, 100-fold lower than without a pre-cooking step (Table 5-10). It can be

explained because, from the results from previous studies (Hewitt & Greening, 2006), pre-cooking by

boiling at 90-100°C for-3-4 min to open the shell reduces NoV particles by at least 2 log10.

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Table 5-9. The NoV-illness cases per year estimated based on the assumption of the most common shellfish cooking methods in Indonesia with the worst-cases scenario

Assumption on shellfish cooking method No of cases Without pre-cooking

All shellfish cooked by boiling (90-100°C for 30 min) 780 All shellfish cooked by steaming (70-80°C for 30 min) 7,800 All shellfish cooked by stir-frying (60-70°C for 30 min) 741,000 Shellfish cooked by mixed method1 250,000

With pre-cooking2 All shellfish cooked by boiling (90-100°C for 30 min) 7.8 All shellfish cooked by steaming (70-80°C for 30 min) 78 All shellfish cooked by stir-frying (60-70°C for 30 min) 7,800 Shellfish cooked by mixed method1 2,600

Note: 1The mixed method was assumed as mixed cooking practices consist of boiling, steaming and stir-frying in equal proportion (33.33% of each cooking method) 2The standard handling procedures of pre-marketed raw or frozen peeled shellfish published by Indonesian government which utilise boiling step (boiling at 90-100°C for 3-4 mins) to open the shell

Results from previous studies by Pintó et al. (2009), which estimate the risk of enteric viruses in

shellfish products in Spain and the documented enteric viruses outbreaks due to shellfish

consumption by Suffredini et al. (2014) were compared to the results from this study. The estimated

NoV attack rates in Indonesia assumed without pre-cooking step were higher than those reports, but

when including the pre-cooking application, the rates were comparable to those estimates in Spain

and Italy (Table 5-10). However, the estimated NoV attack rates in Indonesia were lower than the

attack rate from the recorded HAV cases in China during the outbreaks in 1988 (Halliday et al., 1991)

or from the estimated NoV cases example in UK due to raw shellfish consumption (ICMSF, 2018).

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Table 5-10. The estimated and reported attack rate of enteric virus due to shellfish consumption in different scenario in one-year period

Scenario Attack rate (per 100,000 person) Note

This study

No pre-cooking + boiling only 0.31 Estimated No pre-cooking + steaming only 3.09 Estimated No pre-cooking + stir-frying only 295.43 Estimated No pre-cooking + mixed cooking 99.61 Estimated Pre-cooking + boiling only 0.0031 Estimated Pre-cooking + steaming only 0.031 Estimated Pre-cooking + stir-frying only 3.09 Estimated Pre-cooking + mixed cooking 1.04 Estimated

Other studies

No cooking (Pintó et al., 2009) 0.66-0.91 Estimated Lightly cooking (Pintó et al., 2009) 0.05-0.43 Estimated Well cooking (Pintó et al., 2009 0.01-0.21 Estimated Raw consumption (in UK)* 3,000 Estimated example High pressure process (in UK)* 3.08 Estimated example HAV prevalence studies (in Peru) (Pintó et al., 2009) 3.30-13.30 Estimated Italia outbreaks in 2008 (Suffredini et al., 2014) 2.5 Reported China outbreaks in 1988 (Halliday et al., 1991) 4,083 Reported Note*: Example of risk estimation of NoV cases in UK (ICMSF, 2018)

5.4.2.5. Limitations of the risk assessment and future recommendations

The estimated risk of illnesses and the attack rates due to the consumption of NoV-contaminated

shellfish in Indonesia were different from other outbreak estimates due to enteric viruses which

used a similar risk estimation approach (ICMSF, 2018; Pintó et al., 2009), especially when the pre-

cooking method was not considered. These differences could be due to the various assumptions and

the worst-case scenarios that were used in this current study. In the calculation of virus dose per

serving, this study assumed that the proportion of DT from the total weight of shellfish tissue was

10% (Table 5-1) because the NoV concentration was calculated only from the sample’s DT, while the

study of Pintó et al. (2009) did not use this correction factor and assumed that the level of HAV

contamination in the shellfish DT represented the contamination throughout the flesh of the

individual shellfish. Furthermore, because of the unavailability of an in-vitro assay method to

evaluate the infectivity of the NoV in this study, NoV quantified by RT-qPCR with enzymatic pre-

treatment were assumed as infectious viral particles in the risk estimation. If an in-culturo assay

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becomes available as a standard method to quantify the levels of infectious NoV (such as HAV

quantification assay), the current risk assessment may be improved.

The worst-case scenarios used in this study were made to accommodate data gaps on shellfish

consumption and preparation methods in Indonesia. Based on the prevalence study, the highest

NoV contamination was found in Clams (Table 5-6). Thus, to generate the maximum risk estimate, it

was assumed that all shellfish consumers in Indonesia only consume Clams. Following this

assumption, the highest concentration of NoV in Clams (8,980 copies/g DT) was used in the risk

calculation. In addition, the lowest recovery average was also used in the risk calculation to develop

a worst-case risk estimation.

To resolve these data gaps and refine the risk estimates and potential risk management solutions,

more detailed studies on the volume of different shellfish species consumed by Indonesian

consumer is necessary to follow up the National Food Consumption Survey (SKMI) conducted by the

Ministry of Health. In addition, to get more representative information on the NoV prevalence in

shellfish from Indonesian markets, further studies or surveys should also be carried out in other

Indonesian fish markets. To properly identify the origin of shellfish contamination, direct sampling of

waters from the shellfish growing areas is also suggested. This could also provide information on the

actual level of NoV in shellfish due to faecal-oral transmission (natural contamination).

The current risk assessment focussed on the efficacy of heat treatment as a potential control

measure to reduce NoV contamination in shellfish. The pre-cooking practice, which was proposed by

the Indonesian government for frozen (peeled) shellfish (SNI 3460:2009), was included as an

assumption in the risk calculation. This processing step is intended to open the shellfish shell (BSN,

2009). As shown in Table 5-10, assuming that the pre-cooking step was applied with a further

cooking method, the number of estimated NoV cases as well as the attack rates per year due to

shellfish consumption can be reduced to 100-fold. For example, pre-cooking before boiling reduced

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the estimated cases of illness due to the consumption of NoV-contaminated shellfish from 780 cases

(without pre-cooking) to 7.80 cases (with pre-cooking) per year. This standard processing supports

conclusions from a previous study (Hewitt & Greening, 2006) and guidelines from Codex

Alimentarius Commission (CAC) on the general principles of food hygiene to control viruses in food

(FAO & WHO, 2012) where boiling the shellfish at minimum 3 min resulted in increasing internal

temperature of the shellfish to a minimum of 90°C, and maintaining this internal temperature for

minimum 90 s was recommended to inactivate viruses in most foods.

Following the different methods of cooking that are generally done by shellfish consumers in

Indonesia (Table 5-10), pre-cooked shellfish with further boiling have the lowest risk of residual NoV

contamination in the final product, while the highest-risk product is estimated to be stir-fried

shellfish without pre-cooking. This observation showed that although NoV was found in shellfish sold

in traditional markets in Jakarta, the current processing practices of the consumer will reduce the

NoV contamination in the product. Furthermore, reduction levels are dependent upon the different

cooking methods. It can be suggested that consumer should pre-cook their shellfish before further

cooking and that boiling is preferable to other cooking methods to reduce the level of NoV

contamination. It is also suggested that pre-cooking of raw shellfish should be done in the processing

facilities before the product is sold, particularly for shellfish harvested from polluted sites such as

Jakarta Bay.

However, if the consumption of raw shellfish, such as oysters, becomes popular in Indonesia in the

future, the risk of illnesses due to shellfish consumption might increase beyond the estimates

provided in this study, especially if the shellfish are harvested from polluted sites such as Jakarta

Bay. In this scenario, the pre-cooking practices and cooking methods will not be applied, thus the

risk of NoV infection will need to be re-calculated but would be expected to be tens-of-times higher

per serving. In the absence of cooking steps, the quality assurance of this product from farm to fork

will need to be well-monitored and controlled, e.g., when the shellfish growing/harvesting sites are

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determined as “off-limit” by the competent authorities, following the sites’ closure, the products are

not allowed to be harvested and marketed.

5.5. Conclusion

Frequent NoV contamination was observed in shellfish obtained from traditional markets in Jakarta,

which most likely are harvested from Jakarta Bay. Findings from the risk assessment presented as

part of this study emphasized the value of implementing pre-cooking practices by producers and

consumers, to reduce the level of NoV contamination in the shellfish, thus reducing the estimated

risk of illness. Furthermore, based on the set of assumptions and scenarios in this risk assessment

study, different cooking methods (i.e., boiling, steaming and stir-frying) affects the number of

estimated the risk of NoV cases and the attack rates, with the shellfish boiled for 30 minutes having

the lowest risk product of NoV contamination, and of causing illness to consumers.

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Chapter 6. General discussion and conclusions

6.1. General discussion

As reviewed in Chapter 1 (Section 1.1), NoV remains the leading causative agent of viral

gastroenteritis outbreaks, and subsequent health and economic losses worldwide. Most of the

outbreaks are caused by person-to-person and faecal-to-oral transmission through water and

environmental contamination, whereas some of the outbreaks were associated with consumption of

NoV-contaminated food (Glass et al., 2009; Verhoef et al., 2015). NoV can be introduced into water

via sewage overflows or contaminated water from the surroundings (Aw et al., 2009; Rodríguez et

al., 2012; Wyn-Jones et al., 2011; Yang et al., 2012). Hence, raw or fresh food such as shellfish,

produce and fruit which are grown or harvested, irrigated, handled and processed with NoV-

contaminated water have become the most common source of food-borne NoV infection.

Moreover, food that is prepared and handled by NoV-infected persons (both symptomatic and

asymptomatic) can also contribute to the NoV infection.

Compared to other aquatic animals, shellfish are more susceptible to NoV contamination, due to

their filter feeding behaviour which enables them to accumulate different types of suspended

particles from their aqueous environment, including bacteria and virus (Lees, 2000). An increasing

trend in shellfish consumption and production in Indonesia has been documented since the early

2000’s. The source of Indonesian shellfish is mainly from domestic production by shellfish farming or

wild catch (FAO, 2015). Some of the harvesting sites have been heavily contaminated with sewage

overflow from the rivers, such as Jakarta Bay (Dsikowitzky et al., 2016). Currently, shellfish species

harvested and caught from the Jakarta Bay (i.e., Green Mussel, Blood Cockle and Hard Clam) are

commonly found in the traditional fish markets close to Jakarta Bay (i.e., Cilincing and Muara Kamal).

It is, therefore, likely that these shellfish are being contaminated by faecal sewage containing enteric

viruses including NoV.

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Generally, shellfish in Indonesia are cooked before consumption, however, the risk of NoV

contamination might remain due to inadequate cooking of the contaminated shellfish, secondary

transmission route through cross-contamination between shellfish during washing steps or

contamination from an infected food handler. However, a risk assessment of NoV from shellfish in

Indonesian fish market is not yet available, hence, there is a need to develop this risk assessment

especially for shellfish from traditional fish markets (i.e., Jakarta and Panimbang). Such a food safety

risk assessment will provide the competent authorities in Indonesia (both local and central

government) with information about the estimated magnitude of risk due to consumption of NoV-

contaminated shellfish as well as potential control strategies for NoV foodborne cases.

The EFSA recommended investigation of NoV levels in shellfish products following several incidents

of foodborne NoV illnesses related to the consumption of raw shellfish, using robust methods for

NoV identification and quantification (EFSA Panel on Biological Hazards (BIOHAZ), 2012). However,

these efforts remain challenging because a standard quantification assay based on the cell-culture

system as a robust quantification method is currently unavailable. As a consequence, a molecular-

based method such as RT-qPCR has been used as the gold standard assay for detection and

quantification of NoV (ISO, 2013, 2017; Kirby & Iturriza-Gómara, 2012; Vinjé, 2015). One of the

limitations of using RT-qPCR in the quantitative analysis is the inability of this method to distinguish

between infectious and non-infectious viral particles (Knight et al., 2012; Richards, 1999) which

could lead to overestimation of NoV and the related risks and provided inaccurate information for

the decision making process.

Chapter 2 of this thesis addressed the above issue by proposing enzymatic pre-treatment to improve

the ability of RT-qPCR to differentiate the infectious from non-infectious viral RNA using MS2

bacteriophage (MS2) as a cultivable NoV surrogate. The results showed that the performance of RT-

qPCR without enzymatic pre-treatment was comparable to the plaque assay method only for

quantification of non-heated MS2 (presumed only infectious viruses were present), but was not

comparable for the quantification of infectious MS2 after heat treatment where both infectious and

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non-infectious viral particles were present. In addition, by comparing the result of RT-qPCR with the

culture-based method (plaque assay), the application of RNase as enzymatic pre-treatment was able

to reduce the overestimation of “infectious” viral particles that survive from the treatment. The

ability of RNase to reduce the overestimation of the infectious viral particles can be explained by its

ability to degrade the RNA from non-infectious viral particles lacking capsid protein (Brié et al., 2016;

Pecson et al., 2009), and thus only to quantify RNA from infectious viral particles.

However, without a further step to inactivate RNase by RNasin treatment during the enzymatic pre-

treatment in this study, underestimation of the infectious viral particles was observed using RT-qPCR

assay. The finding of this study showed that the application of RNase followed by RNasin prior to

RNA extraction were able to reduce the overestimation of infectious MS2 from heat treatment

which is confirmed by the comparable results obtained from the RT-qPCR method (with enzymatic

pre-treatment) compared to the plaque assay. Hence, this RT-qPCR assay with enzymatic pre-

treatment (RNase followed by RNasin prior to RNA extraction) was proposed to enumerate

infectious viral particles from thermal or chlorination treatments, as described in Chapters 3 and 4.

This assay (pre-treatment RT-qPCR) was also used to in the prevalence study (Chapter 5).

Quantification using this assay was able to avoid over-estimation, thus provided reliable results on

the level of NoV in shellfish available at retail markets in Indonesia.

Based on the available records, most NoV outbreaks are related to the consumption of raw-

contaminated shellfish (Huppatz et al., 2008; Lodo et al., 2014; Morse et al., 1986; Westrell et al.,

2010), however undercooked shellfish is also reported to cause illnesses (Alfano-Sobsey et al., 2012;

Richards, 2006). To overcome this problem, thermal inactivation has been considered as one of the

most effective treatments to reduce or eliminate enteric virus contamination (Bertrand et al., 2012;

Richards et al., 2010; Teixeira, 2015). Heating the shellfish before consumption is an acceptable

approach in Indonesia, because the majority of shellfish consumers in Indonesia cook their shellfish

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before eating, therefore the heat treatment is unlikely to affect their perception of organoleptic

quality of the final product.

The application of heat to inactivate NoV and MS2 in buffered media and artificially contaminated

mussel (Mytilus galloprovincialis) was evaluated in Chapter 3. The heat inactivation kinetics of the

two viruses were compared using linear (first-order kinetic) and non-linear (Weibull and Biphasic)

models. The heating temperatures explored, 60, 72 and 90°C for different contact times,

represented the cooking processes of stir-frying, steaming and boiling, as the most common cooking

practices of shellfish in Indonesia.

In general, tailing phenomena were observed in all inactivation curves of NoV and MS2 in both

matrices (buffered media and mussel matrix). These findings agree with the observations from

previous thermal inactivation studies (Araud et al., 2016; Bozkurt et al., 2013, 2014a) where there

were more heat-resistant subpopulations present during viral inactivation treatments. As a

consequence, the non-linear models (Weibull and Biphasic) which have lower RMSE values,

performed better in the prediction of thermal inactivation kinetics of the viruses than log-linear

models (first-order kinetic).

Although those non-linear models were appropriate to describe the thermal inactivation curves of

NoV and MS2 for the full duration of the treatment, only the Biphasic model was able to predict the

rates of NoV elimination in both matrices after an extended period. Hence, this model was used to

predict two and four log10 reduction (2D and 4D) of NoV in both matrices. When a non-linear model

is the best to describe the survival curves, the specific viral log10 reductions were best predicted by

direct calculation of determined values (such as 2D, 3D or 4D) from the equation rather than

multiplying the D values (1D) obtained from the models with the targeted log10 reductions (such as

2, 3 or 4) to avoid over or under-estimation. For instance, the time for 4 log10 reductions (4D)

calculated from the equation was not equal to the value of D values (1D) multiplies by 4 because the

responses were not log-linear. Thus, when a food safety objective is determined by the minimum

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requirement of a specific log10 reductions during processing for risk elimination purposes, the use of

targeted D values (e.g., 2D, 3D or 4D) predicted by non-linear models could prevent the

overestimation of viral inactivation in cases where a tailing phenomenon is observed.

A difference in the efficacy of thermal inactivation against NoV and MS2 was observed in this study.

Overall, MS2 was more susceptible than NoV to heat treatment in both matrices (buffered media

and mussel matrix) and at temperatures between 60 and 90 °C and had higher z, D, 2D and 4D

values than NoV. It has been shown that the difference in viral resistance toward environmental

stress between virus species or even strains is determined by capsid protein and genomes structure

of the virus (Thurman & Gerba, 1988). Therefore, MS2 may not be a good candidate for NoV

surrogate in thermal inactivation studies. The efficacy of thermal inactivation is also influenced by

the matrix, i.e. both viruses were generally more resistant towards heat treatment in a complex

matrix (mussel), which is potentially due to the presence of protein and fat in the mussel that

protect the viral particles from heat (Bozkurt et al., 2014b; Croci et al., 2012).

Another source of NoV contamination in food, identified as secondary route of contamination, is

cross-contamination from food handlers or other contaminated products or equipment, which may

occur during harvesting, handling or processing (Bellou et al., 2013; Hall et al., 2012; Polo et al.,

2015; Rodríguez-Lázaro et al., 2012). In this case, the use of disinfectants such as chlorine to prevent

cross-contamination is recommended. The considered concentrations of ClO2 to be used as

disinfectant in water are between 5-100 ppm (FAO & WHO, 2009), while the USFDA recommended

the level of ClO2 residue is less than 3 ppm (CFR 173.300) (USFDA, 2018). To this end, Chapter 4

evaluated the efficacy of chlorine dioxide (ClO2) treatment at concentrations of 10, 20 and 40 ppm at

20 ± 1°C (pH 6.9 ± 2) with a range of contact times, to inactivate NoV and MS2 in buffered media and

mussel (Mytilus galloprovincialis) matrix. Hom, Weibull and Biphasic models were used to estimate

the log10 reduction of viral particles over time, while a first-order kinetic model was used to calculate

ClO2 decay rate. Overall, the viral reduction curves that were generated from the number of

infectious viral particles plotted against time exposure were better fitted by Hom than other models,

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especially for NoV. For some MS2 treatments (20 and 40 ppm), the Biphasic model was the best to

predict MS2 reduction as a function of time. From the observed data, the highest concentration of

ClO2 (40 ppm) with the longest exposure (120 min) produces the highest log10 reduction of the viral

particles with the ClO2 residue <2 ppm in both matrices, thus this treatment has potential to be used

as disinfectant, as considered by the FAO/WHO (FAO & WHO, 2009) and to meet the minimum

residue required by the USFDA (USFDA, 2018).

A matrix effect was observed in the decay of ClO2, where the decay rate was higher in complex

(mussel) than in simple matrix (buffered media), presumably due to the presence of higher loads of

organic matter. As a consequence, the rate of viral reduction of NoV or MS2 was significantly higher

(P<0.05) in buffered media than in mussel matrix from identical treatments. In this study, the decay

of ClO2 over time was presumed to be one of the causes of the tailing phenomenon in all inactivation

curves predicted by the three models (Hom, Weibull and Biphasic). This assumption is in agreement

with the findings from previous studies (Thurston-Enriquez et al., 2003), where one of the factors

that contributed to the tailing phenomenon is the decrease of disinfectant concentration over the

time (Thurman & Gerba, 1988).

The results of the current study provide evidence that the efficacy of ClO2 treatment varies between

viruses. MS2 was generally more susceptible than NoV to ClO2 treatment in both matrices as MS2

obtained a higher log10 reduction than NoV for the same treatments at the same matrix. This

difference in viral response to disinfectant was similar to the viral response towards heat treatment,

which can be explained by the difference in viral protein structures as previously described (Sigstam

et al., 2013; Wigginton et al., 2012). Based on the observation in this thesis together with those

previous studies, MS2, which is less resistant toward heat and ClO2 treatment, may not be suitable as

a NoV surrogate to generate viral particles inactivation kinetics by these treatments. Thus, the use of

MS2 as surrogate in NoV inactivation studies should be performed with caution, to avoid

overestimation of the treatment efficacy. However, MS2 could be a useful enteric virus surrogate to

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describe general trends and mechanisms of enteric viral inactivation studies, especially using heat

and ClO2 treatments.

As part of this study, a prevalence study of NoV in shellfish from Indonesian markets was conducted

in 2016 and 2017 (Chapter 5). The aims of this study were to specifically investigate the presence of

NoV and its level in the shellfish sold in two different places (Jakarta and Panimbang) and two

different market types (“traditional” and “modern”) to indicate the source of shellfish from less or

more polluted areas.

All NoV-contaminated shellfish observed in this study were collected from traditional markets in

Jakarta (Cilincing and Muara Kamal) that represented shellfish harvested from a polluted

environment (Jakarta Bay). The level of NoV concentration in the contaminated shellfish varied

between 1.43 to 3.95 log10 copies/g DT. By adjusting these values with the lowest average of

acceptable viral extraction efficiency (17.7%), the estimated NoV concentration were between 2.20

to 4.72 log10 copies/g DT. Furthermore, no NoV were detected in shellfish collected from Panimbang

fish market or from a modern market from Jakarta. Amongst different shellfish species, the highest

NoV prevalence was found in Green Mussel (Perna viridis). This finding might be correlated with the

fact that Green Mussel is the only species farmed in Jakarta Bay for over three decades, while other

species such as Blood Cockle and Oriental Hard Clam are wild- captured shellfish. Thus, Green

Mussel becomes the predominant shellfish species in the “traditional” markets and that are likely to

be more exposed to viral contamination than other species. In addition, both NoV purified from the

positive samples were identified as GII.4. This genotype (GII.4) has been reported worldwide as the

predominant strain in the NoV genogroup II (GII) that was responsible for many human NoV-

gastroenteritis outbreaks caused by either person-to-person transmission or food contamination

(Baert et al., 2009; Bernard et al., 2014; Bull et al., 2006; Fitzgerald et al., 2014; White, 2014; Zheng

et al., 2010).

131

Based on the Decree of the Indonesian Minister of MAF no. KEP.17/MEN/2004 (MMAF, 2004)

regarding the Indonesian shellfish sanitation system, shellfish farming activities are prohibited in

particular areas which have a high level of faecal contamination in the water (> 300 MPN

coliforms/100 ml) and an excessive level of PSP toxin in the shellfish (> 80µg/100 g of shellfish meat).

Such activities are also prohibited in areas that have not been assessed for the sanitation

compliance. Jakarta Bay plays important roles in different sectors, including the economic,

transportation, tourism and fishery sectors, however the Bay has been experiencing heavy pollution

from domestic and industrial activities in the surrounding areas (Arifin, 2004; Dsikowitzky et al.,

2016; Siregar et al., 2016). Therefore, the Indonesian MMAF has proposed Banten Bay as one of the

potential replacements for shellfish growing sites in Jakarta Bay (Andriyanto, 2018). Results from this

study which showed that NoV was not found in shellfish from Panimbang market (harvested from

Banten Bay which is considered a less polluted area), support this strategy.

Enteric viruses including NoV were not considered as potential microbial contaminants of raw or

frozen shellfish in the Indonesian shellfish sanitation system (MMAF, 2004) as well as in the standard

processing of frozen (SNI 3460.1 to 3: 2009) and canned shellfish (SNI 3919.1-3:2009)(BSN, 2009).

Moreover, regulations that contain minimum safety requirements specifically for viral contamination

parameter in fresh shellfish sold in Indonesia do not exist. Therefore, the quantitative microbial food

safety risk assessment (QMFSRA) to estimate the risk of NoV infection from consuming shellfish from

Indonesia, presented in Chapter 5, could provide science-based information to assist Indonesian

regulatory bodies to establish relevant regulations and develop a management control system for

NoV in shellfish.

In this study, a risk assessment of NoV in shellfish from Indonesian markets was performed to

estimate risks and to provide mitigation strategies. Since several data such as information on the

Indonesian NoV outbreak cases, incidences of NoV illness related to shellfish consumption in

Indonesia, and the proportion of shellfish species consumed by the Indonesian population, were not

available, a deterministic approach was used to develop the risk assessment. To estimate the risk of

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NoV cases per year in Indonesia due to consumption of contaminated shellfish, some data from

previous studies and adjusted parameters (based on several assumptions and worst-case scenario)

were incorporated, while the data for NoV inactivation by thermal inactivation (Chapter 3 of this

thesis) was used to calculate the potential NoV reduction after handling and cooking of the

commodities.

The potential NoV-contaminated servings of shellfish per year in Indonesia were estimated as 8.17

million servings. The expected number of infections per year due to the consumption of NoV-

contaminated shellfish without pre-cooking step in Indonesia was estimated to be 100-folds higher

than the pre-cooked shellfish. This risk estimate was based on the assumed proportion of shellfish

cooking methods and the worst-case scenario i.e., the highest NoV contamination level, the lowest

average of extraction recovery and the highest prevalence data, were used as the input parameters.

By the non-pre-cooked following with mixed cooking method assumption and the worst-case

scenario, the estimated NoV attack rate (100 cases per 100.000 population) per year in this study

were higher than the estimated HAV attack rate in Spain (Pintó et al., 2009). The estimated attack

rate of HAV in Spain was calculated using an assumption of mixed format of shellfish consumption

(i.e., uncooked, lightly and well-cooked). However, when the pre-cooking was included in the

assumption in the risk calculation as an additional step before the different cooking method, the

estimate attack rates of NoV due to shellfish consumption in Indonesia was comparable to the

estimate of HAV attack rates in Spain (Pintó et al., 2009). Moreover, the estimated NoV attack rates

in the current study was lower than those of estimated NoV incidences in UK due to consumption of

contaminated raw shellfish (ICMSF, 2018) or the recorded enteric outbreak due to shellfish

consumption in China (Halliday et al., 1991). It is worth noting that when the pre-cooking is applied

before cooking step (with different methods i.e., boiling, steaming, stir-frying or “mixed”) by

consumer or frozen shellfish producer in Indonesia, this step could potentially reduce the incidences

of NoV outbreak due to shellfish consumption, and prevent the enteric viruses outbreak such as the

reported case in China (Halliday et al., 1991).

133

6.2. Conclusion

This thesis reported the application of RT-qPCR with enzymatic pre-treatment (RNase followed by

RNasin) as a reliable method to quantify infectious viral particles (NoV and MS2) for inactivation

studies in both buffered media and mussel matrix. The proposed method was also able to assist the

NoV quantification in the prevalence study, which were used to support the QMFSRA of NoV in

shellfish sold in fish markets in Indonesia. In general, MS2 has different resistance than NoV toward

heat and ClO2 treatment, thus this bacteriophage may not be the best candidate as a NoV surrogate

especially for inactivation studies. Results from the viral inactivation studies confirmed the presence

of a matrix effect and tailing phenomenon during the treatment. Hence, the non-linear model such

as Biphasic model is suggested as a robust model to be applied to predict and to calculate the

thermal inactivation kinetics, while Hom’s model is considered as the best model to predict ClO2

inactivation kinetics of the virus. The improved quantification method (RTqPCR with enzymatic pre-

treatment) could be used to minimise over or underestimation of NoV risk in shellfish, while in-vitro

assay has not been available as to quantify the infectious NoV. Understanding the kinetic of the

viruses could also support the evaluation of proposed control measures to reduce or to eliminate

NoV contamination. Further incorporation of these information into QMFSRA could finally

contribute to a better estimation of the risk NoV illnesses in a given population.

The prevalence study indicated the presence of NoV GII.4 in Green Mussel (Perna viridis) harvested

from Jakarta Bay. This genotype is also the most common cause of NoV infection worldwide, and this

highlights the importance of regular monitoring and surveillance of NoV in shellfish products (before

they are distributed) in addition to the well-established monitoring of biotoxin, heavy metals and

coliforms in these shellfish growing sites.

The risk assessment suggested that the application of heat treatment (boiling the pre-marketed

shellfish) can be used as a control measure to reduce the number of contaminated NoV, and thus

lower the risk of NoV infection. Besides, based on the evaluation of ClO2 efficacy to reduce viral

134

contamination, this substance could potentially be used as a disinfectant during shellfish handling

and processing to reduce NoV contamination from the secondary route (from infected food handler

and cross-contamination). However, further studies which incorporate results from the ClO2

inactivation study into the shellfish processing plan in Indonesia is needed to estimate the risk

reductions after application of this treatment.

To overcome the limitations from the current QMFSRA, scientific investigations on the NoV dose-

response relationship in Indonesia is needed. Furthermore, integrated approaches to collect and to

record information on the proportion of shellfish consumption format of the Indonesian consumers

could enhance the accuracy and validity of the NoV risk estimate. Despite the limitations in the risk

assessment of this study, this thesis provided science-based evidence which can be applied to

improve the management of the quality and safety of shellfish from food-borne NoV, in Indonesia

and especially from “traditional” markets in Jakarta.

135

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